Internet Anonymity: Pew: Privacy.Importance regression:: Pew_Anonymity_2016
bdanalytics
Date: (Sat) Apr 23, 2016
Introduction:
Data: Source: Training: https://d37djvu3ytnwxt.cloudfront.net/asset-v1:MITx+15.071x_3+1T2016+type@asset+block/AnonymityPoll.csv
New:
Time period:
Synopsis:
Based on analysis utilizing <> techniques,
Summary of key steps & error improvement stats:
Prediction Accuracy Enhancement Options:
- transform.data chunk:
- derive features from multiple features
- manage.missing.data chunk:
- Not fill missing vars
- Fill missing numerics with a different algorithm
- Fill missing chars with data based on clusters
[](.png)
Potential next steps include:
- Organization:
- Categorize by chunk
- Priority criteria:
- Ease of change
- Impacts report
- Cleans innards
- Bug report
- all chunks:
- at chunk-end rm(!glb_)
- manage.missing.data chunk:
- cleaner way to manage re-splitting of training vs. new entity
- extract.features chunk:
- Add n-grams for glbFeatsText
- “RTextTools”, “tau”, “RWeka”, and “textcat” packages
- Add n-grams for glbFeatsText
- fit.models chunk:
- Classification: Plot AUC Curves for all models & highlight glbMdlSel
- Prediction accuracy scatter graph:
- Add tiles (raw vs. PCA)
- Use shiny for drop-down of “important” features
Use plot.ly for interactive plots ?
- Change .fit suffix of model metrics to .mdl if it’s data independent (e.g. AIC, Adj.R.Squared - is it truly data independent ?, etc.)
- create a custom model for rpart that has minbucket as a tuning parameter
varImp for randomForest crashes in caret version:6.0.41 -> submit bug report
- Probability handling for multinomials vs. desired binomial outcome
- ROCR currently supports only evaluation of binary classification tasks (version 1.0.7)
extensions toward multiclass classification are scheduled for the next release
- fit.all.training chunk:
- myplot_prediction_classification: displays ‘x’ instead of ‘+’ when there are no prediction errors
- Compare glb_sel_mdl vs. glb_fin_mdl:
- varImp
- Prediction differences (shd be minimal ?)
- Move glb_analytics_diag_plots to mydsutils.R: (+) Easier to debug (-) Too many glb vars used
- Add print(ggplot.petrinet(glb_analytics_pn) + coord_flip()) at the end of every major chunk
- Parameterize glb_analytics_pn
- Move glb_impute_missing_data to mydsutils.R: (-) Too many glb vars used; glb_<>_df reassigned
- Do non-glm methods handle interaction terms ?
- f-score computation for classifiers should be summation across outcomes (not just the desired one ?)
- Add accuracy computation to glb_dmy_mdl in predict.data.new chunk
- Why does splitting fit.data.training.all chunk into separate chunks add an overhead of ~30 secs ? It’s not rbind b/c other chunks have lower elapsed time. Is it the number of plots ?
- Incorporate code chunks in print_sessionInfo
- Test against
- projects in github.com/bdanalytics
- lectures in jhu-datascience track
Analysis:
rm(list = ls())
set.seed(12345)
options(stringsAsFactors = FALSE)
source("~/Dropbox/datascience/R/mycaret.R")
source("~/Dropbox/datascience/R/mydsutils.R")## Loading required package: caret## Loading required package: lattice## Loading required package: ggplot2source("~/Dropbox/datascience/R/mypetrinet.R")
source("~/Dropbox/datascience/R/myplclust.R")
source("~/Dropbox/datascience/R/myplot.R")
source("~/Dropbox/datascience/R/myscript.R")
source("~/Dropbox/datascience/R/mytm.R")
# Gather all package requirements here
suppressPackageStartupMessages(require(doMC))
glbCores <- 6 # of cores on machine - 2
registerDoMC(glbCores)
suppressPackageStartupMessages(require(caret))
require(plyr)## Loading required package: plyrrequire(dplyr)## Loading required package: dplyr##
## Attaching package: 'dplyr'## The following objects are masked from 'package:plyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionrequire(knitr)## Loading required package: knitrrequire(stringr)## Loading required package: stringr#source("dbgcaret.R")
#packageVersion("snow")
#require(sos); findFn("cosine", maxPages=2, sortby="MaxScore")
# Analysis control global variables
# Inputs
# url/name = "<PathPointer>"; if url specifies a zip file, name = "<filename>";
# or named collection of <PathPointer>s
# sep = choose from c(NULL, "\t")
glbObsTrnFile <- list(url = "https://d37djvu3ytnwxt.cloudfront.net/asset-v1:MITx+15.071x_3+1T2016+type@asset+block/AnonymityPoll.csv"
# or list(url = c(NULL, <.inp1> = "<path1>", <.inp2> = "<path2>"))
, splitSpecs = list(method = "condition" # default when glbObsNewFile is NULL
# select from c("copy", NULL ???, "condition", "sample", )
# ,nRatio = 0.3 # > 0 && < 1 if method == "sample"
# ,seed = 123 # any integer or glbObsTrnPartitionSeed if method == "sample"
,condition = 'is.na(Privacy.Importance)' #; '<var> <condition_operator> <value>'
)
)
glbObsNewFile <- NULL # default OR list(url = "<obsNewFileName>")
glbObsDropCondition <- NULL # : default
# enclose in single-quotes b/c condition might include double qoutes
# use | & ; NOT || &&
# '<condition>'
# 'grepl("^First Draft Video:", glbObsAll$Headline)'
# 'is.na(glbObsAll[, glb_rsp_var_raw])'
# '(is.na(glbObsAll[, glb_rsp_var_raw]) & grepl("Train", glbObsAll[, glbFeatsId]))'
# 'is.na(strptime(glbObsAll[, "Date"], glbFeatsDateTime[["Date"]]["format"], tz = glbFeatsDateTime[["Date"]]["timezone"]))'
#nrow(do.call("subset",list(glbObsAll, parse(text=paste0("!(", glbObsDropCondition, ")")))))
glb_obs_repartition_train_condition <- NULL # : default
# "<condition>"
glb_max_fitobs <- NULL # or any integer
glbObsTrnPartitionSeed <- 123 # or any integer
glb_is_regression <- TRUE; glb_is_classification <- !glb_is_regression;
glb_is_binomial <- NULL # or TRUE or FALSE
glb_rsp_var_raw <- "Privacy.Importance"
# for classification, the response variable has to be a factor
glb_rsp_var <- glb_rsp_var_raw # or "Privacy.Importance.fctr"
# if the response factor is based on numbers/logicals e.g (0/1 OR TRUE/FALSE vs. "A"/"B"),
# or contains spaces (e.g. "Not in Labor Force")
# caret predict(..., type="prob") crashes
glb_map_rsp_raw_to_var <- NULL
# function(raw) {
# return(raw ^ 0.5)
# return(log(raw))
# return(log(1 + raw))
# return(log10(raw))
# return(exp(-raw / 2))
# ret_vals <- rep_len(NA, length(raw)); ret_vals[!is.na(raw)] <- ifelse(raw[!is.na(raw)] == 1, "Y", "N"); return(relevel(as.factor(ret_vals), ref="N"))
# as.factor(paste0("B", raw))
# as.factor(gsub(" ", "\\.", raw))
# }
#if glb_rsp_var_raw is numeric:
#print(summary(glbObsAll[, glb_rsp_var_raw]))
#glb_map_rsp_raw_to_var(tst <- c(NA, as.numeric(summary(glbObsAll[, glb_rsp_var_raw]))))
#if glb_rsp_var_raw is character:
#print(table(glbObsAll[, glb_rsp_var_raw], useNA = "ifany"))
#print(table(glb_map_rsp_raw_to_var(tst <- glbObsAll[, glb_rsp_var_raw]), useNA = "ifany"))
glb_map_rsp_var_to_raw <- NULL
# function(var) {
# return(var ^ 2.0)
# return(exp(var))
# return(10 ^ var)
# return(-log(var) * 2)
# as.numeric(var)
# levels(var)[as.numeric(var)]
# gsub("\\.", " ", levels(var)[as.numeric(var)])
# c("<=50K", " >50K")[as.numeric(var)]
# c(FALSE, TRUE)[as.numeric(var)]
# }
#print(table(glb_map_rsp_var_to_raw(glb_map_rsp_raw_to_var(tst)), useNA = "ifany"))
if ((glb_rsp_var != glb_rsp_var_raw) && is.null(glb_map_rsp_raw_to_var))
stop("glb_map_rsp_raw_to_var function expected")
# List info gathered for various columns
# <col_name>: <description>; <notes>
# Internet.Use: A binary variable indicating if the interviewee uses the Internet, at least occasionally (equals 1 if the interviewee uses the Internet, and equals 0 if the interviewee does not use the Internet).
# Smartphone: A binary variable indicating if the interviewee has a smartphone (equals 1 if they do have a smartphone, and equals 0 if they don't have a smartphone).
# Sex: Male or Female.
# Age: Age in years.
# State: State of residence of the interviewee.
# Region: Census region of the interviewee (Midwest, Northeast, South, or West).
# Conservativeness: Self-described level of conservativeness of interviewee, from 1 (very liberal) to 5 (very conservative).
# Info.On.Internet: Number of the following items this interviewee believes to be available on the Internet for others to see: (1) Their email address; (2) Their home address; (3) Their home phone number; (4) Their cell phone number; (5) The employer/company they work for; (6) Their political party or political affiliation; (7) Things they've written that have their name on it; (8) A photo of them; (9) A video of them; (10) Which groups or organizations they belong to; and (11) Their birth date.
# Worry.About.Info: A binary variable indicating if the interviewee worries about how much information is available about them on the Internet (equals 1 if they worry, and equals 0 if they don't worry).
# Privacy.Importance: A score from 0 (privacy is not too important) to 100 (privacy is very important), which combines the degree to which they find privacy important in the following: (1) The websites they browse; (2) Knowledge of the place they are located when they use the Internet; (3) The content and files they download; (4) The times of day they are online; (5) The applications or programs they use; (6) The searches they perform; (7) The content of their email; (8) The people they exchange email with; and (9) The content of their online chats or hangouts with others.
# Anonymity.Possible: A binary variable indicating if the interviewee thinks it's possible to use the Internet anonymously, meaning in such a way that online activities can't be traced back to them (equals 1 if he/she believes you can, and equals 0 if he/she believes you can't).
# Tried.Masking.Identity: A binary variable indicating if the interviewee has ever tried to mask his/her identity when using the Internet (equals 1 if he/she has tried to mask his/her identity, and equals 0 if he/she has not tried to mask his/her identity).
# Privacy.Laws.Effective: A binary variable indicating if the interviewee believes United States law provides reasonable privacy protection for Internet users (equals 1 if he/she believes it does, and equals 0 if he/she believes it doesn't).
# currently does not handle more than 1 column; consider concatenating multiple columns
# If glbFeatsId == NULL, ".rownames <- as.numeric(row.names())" is the default
glbFeatsId <- NULL # choose from c(NULL : default, "<id_feat>")
glbFeatsCategory <- NULL # choose from c(NULL : default, "<category_feat>")
# User-specified exclusions
glbFeatsExclude <- c(NULL
# Feats that shd be excluded due to known causation by prediction variable
# , "<feat1", "<feat2>"
# Feats that are factors with unique values (as % of nObs) > 49 (empirically derived)
# Feats that are linear combinations (alias in glm)
# Feature-engineering phase -> start by excluding all features except id & category & work each one in
,"State"
)
if (glb_rsp_var_raw != glb_rsp_var)
glbFeatsExclude <- union(glbFeatsExclude, glb_rsp_var_raw)
glbFeatsInteractionOnly <- list()
#glbFeatsInteractionOnly[["<child_feat>"]] <- "<parent_feat>"
glbFeatsDrop <- c(NULL
# , "<feat1>", "<feat2>"
)
glb_map_vars <- NULL # or c("<var1>", "<var2>")
glb_map_urls <- list();
# glb_map_urls[["<var1>"]] <- "<var1.url>"
# Derived features; Use this mechanism to cleanse data ??? Cons: Data duplication ???
glbFeatsDerive <- list();
# glbFeatsDerive[["<feat.my.sfx>"]] <- list(
# mapfn = function(<arg1>, <arg2>) { return(function(<arg1>, <arg2>)) }
# , args = c("<arg1>", "<arg2>"))
#myprint_df(data.frame(ImageId = mapfn(glbObsAll$.src, glbObsAll$.pos)))
#data.frame(ImageId = mapfn(glbObsAll$.src, glbObsAll$.pos))[7045:7055, ]
# character
# mapfn = function(Education) { raw <- Education; raw[is.na(raw)] <- "NA.my"; return(as.factor(raw)) }
# mapfn = function(Week) { return(substr(Week, 1, 10)) }
# mapfn = function(Name) { return(sapply(Name, function(thsName)
# str_sub(unlist(str_split(thsName, ","))[1], 1, 1))) }
# mapfn = function(descriptor) { return(plyr::revalue(descriptor, c(
# "ABANDONED BUILDING" = "OTHER",
# "**" = "**"
# ))) }
# mapfn = function(description) { mod_raw <- description;
# This is here because it does not work if it's in txt_map_filename
# mod_raw <- gsub(paste0(c("\n", "\211", "\235", "\317", "\333"), collapse = "|"), " ", mod_raw)
# Don't parse for "." because of ".com"; use customized gsub for that text
# mod_raw <- gsub("(\\w)(!|\\*|,|-|/)(\\w)", "\\1\\2 \\3", mod_raw);
# Some state acrnoyms need context for separation e.g.
# LA/L.A. could either be "Louisiana" or "LosAngeles"
# modRaw <- gsub("\\bL\\.A\\.( |,|')", "LosAngeles\\1", modRaw);
# OK/O.K. could either be "Oklahoma" or "Okay"
# modRaw <- gsub("\\bACA OK\\b", "ACA OKay", modRaw);
# modRaw <- gsub("\\bNow O\\.K\\.\\b", "Now OKay", modRaw);
# PR/P.R. could either be "PuertoRico" or "Public Relations"
# modRaw <- gsub("\\bP\\.R\\. Campaign", "PublicRelations Campaign", modRaw);
# VA/V.A. could either be "Virginia" or "VeteransAdministration"
# modRaw <- gsub("\\bthe V\\.A\\.\\:", "the VeteranAffairs:", modRaw);
#
# Custom mods
# return(mod_raw) }
# numeric
# Create feature based on record position/id in data
glbFeatsDerive[[".pos"]] <- list(
mapfn = function(.rnorm) { return(1:length(.rnorm)) }
, args = c(".rnorm"))
glbFeatsDerive[[".pos.y"]] <- list(
mapfn = function(.rnorm) { return(1:length(.rnorm)) }
, args = c(".rnorm"))
# Add logs of numerics that are not distributed normally
# Derive & keep multiple transformations of the same feature, if normality is hard to achieve with just one transformation
# Right skew: logp1; sqrt; ^ 1/3; logp1(logp1); log10; exp(-<feat>/constant)
# glbFeatsDerive[["WordCount.log1p"]] <- list(
# mapfn = function(WordCount) { return(log1p(WordCount)) }
# , args = c("WordCount"))
# glbFeatsDerive[["WordCount.root2"]] <- list(
# mapfn = function(WordCount) { return(WordCount ^ (1/2)) }
# , args = c("WordCount"))
# glbFeatsDerive[["WordCount.nexp"]] <- list(
# mapfn = function(WordCount) { return(exp(-WordCount)) }
# , args = c("WordCount"))
#print(summary(glbObsAll$WordCount))
#print(summary(mapfn(glbObsAll$WordCount)))
# If imputation shd be skipped for this feature
# glbFeatsDerive[["District.fctr"]] <- list(
# mapfn = function(District) {
# raw <- District;
# ret_vals <- rep_len("NA", length(raw));
# ret_vals[!is.na(raw)] <- sapply(raw[!is.na(raw)], function(elm)
# ifelse(elm < 10, "1-9",
# ifelse(elm < 20, "10-19", "20+")));
# return(relevel(as.factor(ret_vals), ref = "NA"))
# }
# , args = c("District"))
# If imputation of missing data is not working ...
# glbFeatsDerive[["FertilityRate.nonNA"]] <- list(
# mapfn = function(FertilityRate, Region) {
# RegionMdn <- tapply(FertilityRate, Region, FUN = median, na.rm = TRUE)
#
# retVal <- FertilityRate
# retVal[is.na(FertilityRate)] <- RegionMdn[Region[is.na(FertilityRate)]]
# return(retVal)
# }
# , args = c("FertilityRate", "Region"))
# mapfn = function(HOSPI.COST) { return(cut(HOSPI.COST, 5, breaks = c(0, 100000, 200000, 300000, 900000), labels = NULL)) }
# mapfn = function(Rasmussen) { return(ifelse(sign(Rasmussen) >= 0, 1, 0)) }
# mapfn = function(startprice) { return(startprice ^ (1/2)) }
# mapfn = function(startprice) { return(log(startprice)) }
# mapfn = function(startprice) { return(exp(-startprice / 20)) }
# mapfn = function(startprice) { return(scale(log(startprice))) }
# mapfn = function(startprice) { return(sign(sprice.predict.diff) * (abs(sprice.predict.diff) ^ (1/10))) }
# factor
# mapfn = function(PropR) { return(as.factor(ifelse(PropR >= 0.5, "Y", "N"))) }
# mapfn = function(productline, description) { as.factor(gsub(" ", "", productline)) }
# mapfn = function(purpose) { return(relevel(as.factor(purpose), ref="all_other")) }
# mapfn = function(raw) { tfr_raw <- as.character(cut(raw, 5));
# tfr_raw[is.na(tfr_raw)] <- "NA.my";
# return(as.factor(tfr_raw)) }
# mapfn = function(startprice.log10) { return(cut(startprice.log10, 3)) }
# mapfn = function(startprice.log10) { return(cut(sprice.predict.diff, c(-1000, -100, -10, -1, 0, 1, 10, 100, 1000))) }
# , args = c("<arg1>"))
# multiple args
# mapfn = function(id, date) { return(paste(as.character(id), as.character(date), sep = "#")) }
# mapfn = function(PTS, oppPTS) { return(PTS - oppPTS) }
# mapfn = function(startprice.log10.predict, startprice) {
# return(spdiff <- (10 ^ startprice.log10.predict) - startprice) }
# mapfn = function(productline, description) { as.factor(
# paste(gsub(" ", "", productline), as.numeric(nchar(description) > 0), sep = "*")) }
# mapfn = function(.src, .pos) {
# return(paste(.src, sprintf("%04d",
# ifelse(.src == "Train", .pos, .pos - 7049)
# ), sep = "#")) }
# # If glbObsAll is not sorted in the desired manner
# mapfn=function(Week) { return(coredata(lag(zoo(orderBy(~Week, glbObsAll)$ILI), -2, na.pad=TRUE))) }
# mapfn=function(ILI) { return(coredata(lag(zoo(ILI), -2, na.pad=TRUE))) }
# mapfn=function(ILI.2.lag) { return(log(ILI.2.lag)) }
# glbFeatsDerive[["<var1>"]] <- glbFeatsDerive[["<var2>"]]
# tst <- "descr.my"; args_lst <- NULL; for (arg in glbFeatsDerive[[tst]]$args) args_lst[[arg]] <- glbObsAll[, arg]; print(head(args_lst[[arg]])); print(head(drv_vals <- do.call(glbFeatsDerive[[tst]]$mapfn, args_lst)));
# print(which_ix <- which(args_lst[[arg]] == 0.75)); print(drv_vals[which_ix]);
glbFeatsDateTime <- list()
# Use OlsonNames() to enumerate supported time zones
# glbFeatsDateTime[["<DateTimeFeat>"]] <-
# c(format = "%Y-%m-%d %H:%M:%S" or "%m/%e/%y", timezone = "US/Eastern", impute.na = TRUE,
# last.ctg = FALSE, poly.ctg = FALSE)
glbFeatsPrice <- NULL # or c("<price_var>")
glbFeatsImage <- list() #list(<imageFeat> = list(patchSize = 10)) # if patchSize not specified, no patch computation
glbFeatsText <- list()
Sys.setlocale("LC_ALL", "C") # For english## [1] "C/C/C/C/C/en_US.UTF-8"#glbFeatsText[["<TextFeature>"]] <- list(NULL,
# ,names = myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL,
# <comma-separated-screened-names>
# ))))
# ,rareWords = myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL,
# <comma-separated-nonSCOWL-words>
# ))))
#)
# Text Processing Step: custom modifications not present in txt_munge -> use glbFeatsDerive
# Text Processing Step: universal modifications
glb_txt_munge_filenames_pfx <- "<projectId>_mytxt_"
# Text Processing Step: tolower
# Text Processing Step: myreplacePunctuation
# Text Processing Step: removeWords
glb_txt_stop_words <- list()
# Remember to use unstemmed words
if (length(glbFeatsText) > 0) {
require(tm)
require(stringr)
glb_txt_stop_words[["<txt_var>"]] <- sort(myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
# Remove any words from stopwords
# , setdiff(myreplacePunctuation(stopwords("english")), c("<keep_wrd1>", <keep_wrd2>"))
# Remove salutations
,"mr","mrs","dr","Rev"
# Remove misc
#,"th" # Happy [[:digit::]]+th birthday
# Remove terms present in Trn only or New only; search for "Partition post-stem"
# ,<comma-separated-terms>
# cor.y.train == NA
# ,unlist(strsplit(paste(c(NULL
# ,"<comma-separated-terms>"
# ), collapse=",")
# freq == 1; keep c("<comma-separated-terms-to-keep>")
# ,<comma-separated-terms>
# chisq.pval high (e.g. == 1); keep c("<comma-separated-terms-to-keep>")
# ,<comma-separated-terms>
# nzv.freqRatio high (e.g. >= glbFeatsNzvFreqMax); keep c("<comma-separated-terms-to-keep>")
# ,<comma-separated-terms>
)))))
}
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^man", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
#glbObsAll[glb_post_stem_words_terms_mtrx_lst[[txtFeat]][, 4866] > 0, c(glb_rsp_var, txtFeat)]
# To identify terms with a specific freq
#paste0(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], freq == 1)$term), collapse = ",")
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], freq <= 2)$term), collapse = ",")
#subset(glb_post_stem_words_terms_df_lst[[txtFeat]], term %in% c("zinger"))
# To identify terms with a specific freq &
# are not stemmed together later OR is value of color.fctr (e.g. gold)
#paste0(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], (freq == 1) & !(term %in% c("blacked","blemish","blocked","blocks","buying","cables","careful","carefully","changed","changing","chargers","cleanly","cleared","connect","connects","connected","contains","cosmetics","default","defaulting","defective","definitely","describe","described","devices","displays","drop","drops","engravement","excellant","excellently","feels","fix","flawlessly","frame","framing","gentle","gold","guarantee","guarantees","handled","handling","having","install","iphone","iphones","keeped","keeps","known","lights","line","lining","liquid","liquidation","looking","lots","manuals","manufacture","minis","most","mostly","network","networks","noted","opening","operated","performance","performs","person","personalized","photograph","physically","placed","places","powering","pre","previously","products","protection","purchasing","returned","rotate","rotation","running","sales","second","seconds","shipped","shuts","sides","skin","skinned","sticker","storing","thats","theres","touching","unusable","update","updates","upgrade","weeks","wrapped","verified","verify") ))$term), collapse = ",")
#print(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (freq <= 2)))
#glbObsAll[which(terms_mtrx[, 229] > 0), glbFeatsText]
# To identify terms with cor.y == NA
#orderBy(~-freq+term, subset(glb_post_stop_words_terms_df_lst[[txtFeat]], is.na(cor.y)))
#paste(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], is.na(cor.y))[, "term"]), collapse=",")
#orderBy(~-freq+term, subset(glb_post_stem_words_terms_df_lst[[txtFeat]], is.na(cor.y)))
# To identify terms with low cor.y.abs
#head(orderBy(~cor.y.abs+freq+term, subset(glb_post_stem_words_terms_df_lst[[txtFeat]], !is.na(cor.y))), 5)
# To identify terms with high chisq.pval
#subset(glb_post_stem_words_terms_df_lst[[txtFeat]], chisq.pval > 0.99)
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (chisq.pval > 0.99) & (freq <= 10))$term), collapse=",")
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (chisq.pval > 0.9))$term), collapse=",")
#head(orderBy(~-chisq.pval+freq+term, glb_post_stem_words_terms_df_lst[[txtFeat]]), 5)
#glbObsAll[glb_post_stem_words_terms_mtrx_lst[[txtFeat]][, 68] > 0, glbFeatsText]
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^m", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
# To identify terms with high nzv.freqRatio
#summary(glb_post_stem_words_terms_df_lst[[txtFeat]]$nzv.freqRatio)
#paste0(sort(setdiff(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (nzv.freqRatio >= glbFeatsNzvFreqMax) & (freq < 10) & (chisq.pval >= 0.05))$term, c( "128gb","3g","4g","gold","ipad1","ipad3","ipad4","ipadair2","ipadmini2","manufactur","spacegray","sprint","tmobil","verizon","wifion"))), collapse=",")
# To identify obs with a txt term
#tail(orderBy(~-freq+term, glb_post_stop_words_terms_df_lst[[txtFeat]]), 20)
#mydspObs(list(descr.my.contains="non"), cols=c("color", "carrier", "cellular", "storage"))
#grep("ever", dimnames(terms_stop_mtrx)$Terms)
#which(terms_stop_mtrx[, grep("ipad", dimnames(terms_stop_mtrx)$Terms)] > 0)
#glbObsAll[which(terms_stop_mtrx[, grep("16", dimnames(terms_stop_mtrx)$Terms)[1]] > 0), c(glbFeatsCategory, "storage", txtFeat)]
# Text Processing Step: screen for names # Move to glbFeatsText specs section in order of text processing steps
# glbFeatsText[["<txtFeat>"]]$names <- myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
# # Person names for names screening
# ,<comma-separated-list>
#
# # Company names
# ,<comma-separated-list>
#
# # Product names
# ,<comma-separated-list>
# ))))
# glbFeatsText[["<txtFeat>"]]$rareWords <- myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
# # Words not in SCOWL db
# ,<comma-separated-list>
# ))))
# To identify char vectors post glbFeatsTextMap
#grep("six(.*)hour", glb_txt_chr_lst[[txtFeat]], ignore.case = TRUE, value = TRUE)
#grep("[S|s]ix(.*)[H|h]our", glb_txt_chr_lst[[txtFeat]], value = TRUE)
# To identify whether terms shd be synonyms
#orderBy(~term, glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^moder", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ])
# term_row_df <- glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^came$", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ]
#
# cor(glb_post_stop_words_terms_mtrx_lst[[txtFeat]][glbObsAll$.lcn == "Fit", term_row_df$pos], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
# To identify which stopped words are "close" to a txt term
#sort(cluster_vars)
# Text Processing Step: stemDocument
# To identify stemmed txt terms
#glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^la$", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ]
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^con", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
#glbObsAll[which(terms_stem_mtrx[, grep("use", dimnames(terms_stem_mtrx)$Terms)[[1]]] > 0), c(glbFeatsId, "productline", txtFeat)]
#glbObsAll[which(TfIdf_stem_mtrx[, 191] > 0), c(glbFeatsId, glbFeatsCategory, txtFeat)]
#glbObsAll[which(glb_post_stop_words_terms_mtrx_lst[[txtFeat]][, 6165] > 0), c(glbFeatsId, glbFeatsCategory, txtFeat)]
#which(glbObsAll$UniqueID %in% c(11915, 11926, 12198))
# Text Processing Step: mycombineSynonyms
# To identify which terms are associated with not -> combine "could not" & "couldn't"
#findAssocs(glb_full_DTM_lst[[txtFeat]], "not", 0.05)
# To identify which synonyms should be combined
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^c", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
chk_comb_cor <- function(syn_lst) {
# cor(terms_stem_mtrx[glbObsAll$.src == "Train", grep("^(damag|dent|ding)$", dimnames(terms_stem_mtrx)[[2]])], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
print(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], term %in% syn_lst$syns))
print(subset(get_corpus_terms(tm_map(glbFeatsTextCorpus[[txtFeat]], mycombineSynonyms, list(syn_lst), lazy=FALSE)), term == syn_lst$word))
# cor(terms_stop_mtrx[glbObsAll$.src == "Train", grep("^(damage|dent|ding)$", dimnames(terms_stop_mtrx)[[2]])], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
# cor(rowSums(terms_stop_mtrx[glbObsAll$.src == "Train", grep("^(damage|dent|ding)$", dimnames(terms_stop_mtrx)[[2]])]), glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
}
#chk_comb_cor(syn_lst=list(word="cabl", syns=c("cabl", "cord")))
#chk_comb_cor(syn_lst=list(word="damag", syns=c("damag", "dent", "ding")))
#chk_comb_cor(syn_lst=list(word="dent", syns=c("dent", "ding")))
#chk_comb_cor(syn_lst=list(word="use", syns=c("use", "usag")))
glbFeatsTextSynonyms <- list()
# list parsed to collect glbFeatsText[[<txtFeat>]]$vldTerms
# glbFeatsTextSynonyms[["Hdln.my"]] <- list(NULL
# # people in places
# , list(word = "australia", syns = c("australia", "australian"))
# , list(word = "italy", syns = c("italy", "Italian"))
# , list(word = "newyork", syns = c("newyork", "newyorker"))
# , list(word = "Pakistan", syns = c("Pakistan", "Pakistani"))
# , list(word = "peru", syns = c("peru", "peruvian"))
# , list(word = "qatar", syns = c("qatar", "qatari"))
# , list(word = "scotland", syns = c("scotland", "scotish"))
# , list(word = "Shanghai", syns = c("Shanghai", "Shanzhai"))
# , list(word = "venezuela", syns = c("venezuela", "venezuelan"))
#
# # companies - needs to be data dependent
# # - e.g. ensure BNP in this experiment/feat always refers to BNPParibas
#
# # general synonyms
# , list(word = "Create", syns = c("Create","Creator"))
# , list(word = "cute", syns = c("cute","cutest"))
# , list(word = "Disappear", syns = c("Disappear","Fadeout"))
# , list(word = "teach", syns = c("teach", "taught"))
# , list(word = "theater", syns = c("theater", "theatre", "theatres"))
# , list(word = "understand", syns = c("understand", "understood"))
# , list(word = "weak", syns = c("weak", "weaken", "weaker", "weakest"))
# , list(word = "wealth", syns = c("wealth", "wealthi"))
#
# # custom synonyms (phrases)
#
# # custom synonyms (names)
# )
#glbFeatsTextSynonyms[["<txtFeat>"]] <- list(NULL
# , list(word="<stem1>", syns=c("<stem1>", "<stem1_2>"))
# )
for (txtFeat in names(glbFeatsTextSynonyms))
for (entryIx in 1:length(glbFeatsTextSynonyms[[txtFeat]])) {
glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$word <-
str_to_lower(glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$word)
glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$syns <-
str_to_lower(glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$syns)
}
glbFeatsTextSeed <- 181
# tm options include: check tm::weightSMART
glb_txt_terms_control <- list( # Gather model performance & run-time stats
# weighting = function(x) weightSMART(x, spec = "nnn")
# weighting = function(x) weightSMART(x, spec = "lnn")
# weighting = function(x) weightSMART(x, spec = "ann")
# weighting = function(x) weightSMART(x, spec = "bnn")
# weighting = function(x) weightSMART(x, spec = "Lnn")
#
weighting = function(x) weightSMART(x, spec = "ltn") # default
# weighting = function(x) weightSMART(x, spec = "lpn")
#
# weighting = function(x) weightSMART(x, spec = "ltc")
#
# weighting = weightBin
# weighting = weightTf
# weighting = weightTfIdf # : default
# termFreq selection criteria across obs: tm default: list(global=c(1, Inf))
, bounds = list(global = c(1, Inf))
# wordLengths selection criteria: tm default: c(3, Inf)
, wordLengths = c(1, Inf)
)
glb_txt_cor_var <- glb_rsp_var # : default # or c(<feat>)
# select one from c("union.top.val.cor", "top.cor", "top.val", default: "top.chisq", "sparse")
glbFeatsTextFilter <- "top.chisq"
glbFeatsTextTermsMax <- rep(10, length(glbFeatsText)) # :default
names(glbFeatsTextTermsMax) <- names(glbFeatsText)
# Text Processing Step: extractAssoc
glbFeatsTextAssocCor <- rep(1, length(glbFeatsText)) # :default
names(glbFeatsTextAssocCor) <- names(glbFeatsText)
# Remember to use stemmed terms
glb_important_terms <- list()
# Text Processing Step: extractPatterns (ngrams)
glbFeatsTextPatterns <- list()
#glbFeatsTextPatterns[[<txtFeat>>]] <- list()
#glbFeatsTextPatterns[[<txtFeat>>]] <- c(metropolitan.diary.colon = "Metropolitan Diary:")
# Have to set it even if it is not used
# Properties:
# numrows(glb_feats_df) << numrows(glbObsFit
# Select terms that appear in at least 0.2 * O(FP/FN(glbObsOOB)) ???
# numrows(glbObsOOB) = 1.1 * numrows(glbObsNew) ???
glb_sprs_thresholds <- NULL # or c(<txtFeat1> = 0.988, <txtFeat2> = 0.970, <txtFeat3> = 0.970)
glbFctrMaxUniqVals <- 20 # default: 20
glb_impute_na_data <- TRUE # or TRUE
glb_mice_complete.seed <- 144 # or any integer
glb_cluster <- FALSE # : default or TRUE
glb_cluster.seed <- 189 # or any integer
glb_cluster_entropy_var <- NULL # c(glb_rsp_var, as.factor(cut(glb_rsp_var, 3)), default: NULL)
glbFeatsTextClusterVarsExclude <- FALSE # default FALSE
glb_interaction_only_feats <- NULL # : default or c(<parent_feat> = "<child_feat>")
glbFeatsNzvFreqMax <- 19 # 19 : caret default
glbFeatsNzvUniqMin <- 10 # 10 : caret default
glbRFESizes <- list()
#glbRFESizes[["mdlFamily"]] <- c(4, 8, 16, 32, 64, 67, 68, 69) # Accuracy@69/70 = 0.8258
glbObsFitOutliers <- list()
# If outliers.n >= 10; consider concatenation of interaction vars
# glbObsFitOutliers[["<mdlFamily>"]] <- c(NULL
# is.na(.rstudent)
# max(.rstudent)
# is.na(.dffits)
# .hatvalues >= 0.99
# -38,167,642 < minmax(.rstudent) < 49,649,823
# , <comma-separated-<glbFeatsId>>
# )
glbObsTrnOutliers <- list()
glbObsTrnOutliers[["Final"]] <- union(glbObsFitOutliers[["All.X"]],
c(NULL
))
# influence.measures: car::outlier; rstudent; dffits; hatvalues; dfbeta; dfbetas
#mdlId <- "All.X##rcv#glm"; obs_df <- fitobs_df
#mdlId <- "RFE.X.glm"; obs_df <- fitobs_df
#mdlId <- "Final.glm"; obs_df <- trnobs_df
#mdlId <- "CSM2.X.glm"; obs_df <- fitobs_df
#print(outliers <- car::outlierTest(glb_models_lst[[mdlId]]$finalModel))
#mdlIdFamily <- paste0(head(unlist(str_split(mdlId, "\\.")), -1), collapse="."); obs_df <- dplyr::filter_(obs_df, interp(~(!(var %in% glbObsFitOutliers[[mdlIdFamily]])), var = as.name(glbFeatsId))); model_diags_df <- cbind(obs_df, data.frame(.rstudent=stats::rstudent(glb_models_lst[[mdlId]]$finalModel)), data.frame(.dffits=stats::dffits(glb_models_lst[[mdlId]]$finalModel)), data.frame(.hatvalues=stats::hatvalues(glb_models_lst[[mdlId]]$finalModel)));print(summary(model_diags_df[, c(".rstudent",".dffits",".hatvalues")])); table(cut(model_diags_df$.hatvalues, breaks=c(0.00, 0.98, 0.99, 1.00)))
#print(subset(model_diags_df, is.na(.rstudent))[, glbFeatsId])
#print(model_diags_df[which.max(model_diags_df$.rstudent), ])
#print(subset(model_diags_df, is.na(.dffits))[, glbFeatsId])
#print(model_diags_df[which.min(model_diags_df$.dffits), ])
#print(subset(model_diags_df, .hatvalues > 0.99)[, glbFeatsId])
#dffits_df <- merge(dffits_df, outliers_df, by="row.names", all.x=TRUE); row.names(dffits_df) <- dffits_df$Row.names; dffits_df <- subset(dffits_df, select=-Row.names)
#dffits_df <- merge(dffits_df, glbObsFit, by="row.names", all.x=TRUE); row.names(dffits_df) <- dffits_df$Row.names; dffits_df <- subset(dffits_df, select=-Row.names)
#subset(dffits_df, !is.na(.Bonf.p))
#mdlId <- "CSM.X.glm"; vars <- myextract_actual_feats(row.names(orderBy(reformulate(c("-", paste0(mdlId, ".imp"))), myget_feats_imp(glb_models_lst[[mdlId]]))));
#model_diags_df <- glb_get_predictions(model_diags_df, mdlId, glb_rsp_var)
#obs_ix <- row.names(model_diags_df) %in% names(outliers$rstudent)[1]
#obs_ix <- which(is.na(model_diags_df$.rstudent))
#obs_ix <- which(is.na(model_diags_df$.dffits))
#myplot_parcoord(obs_df=model_diags_df[, c(glbFeatsId, glbFeatsCategory, ".rstudent", ".dffits", ".hatvalues", glb_rsp_var, paste0(glb_rsp_var, mdlId), vars[1:min(20, length(vars))])], obs_ix=obs_ix, id_var=glbFeatsId, category_var=glbFeatsCategory)
#model_diags_df[row.names(model_diags_df) %in% names(outliers$rstudent)[c(1:2)], ]
#ctgry_diags_df <- model_diags_df[model_diags_df[, glbFeatsCategory] %in% c("Unknown#0"), ]
#myplot_parcoord(obs_df=ctgry_diags_df[, c(glbFeatsId, glbFeatsCategory, ".rstudent", ".dffits", ".hatvalues", glb_rsp_var, "startprice.log10.predict.RFE.X.glmnet", indepVar[1:20])], obs_ix=row.names(ctgry_diags_df) %in% names(outliers$rstudent)[1], id_var=glbFeatsId, category_var=glbFeatsCategory)
#table(glbObsFit[model_diags_df[, glbFeatsCategory] %in% c("iPad1#1"), "startprice.log10.cut.fctr"])
#glbObsFit[model_diags_df[, glbFeatsCategory] %in% c("iPad1#1"), c(glbFeatsId, "startprice")]
# No outliers & .dffits == NaN
#myplot_parcoord(obs_df=model_diags_df[, c(glbFeatsId, glbFeatsCategory, glb_rsp_var, "startprice.log10.predict.RFE.X.glmnet", indepVar[1:10])], obs_ix=seq(1:nrow(model_diags_df))[is.na(model_diags_df$.dffits)], id_var=glbFeatsId, category_var=glbFeatsCategory)
# Modify mdlId to (build & extract) "<FamilyId>#<Fit|Trn>#<caretMethod>#<preProc1.preProc2>#<samplingMethod>"
glb_models_lst <- list(); glb_models_df <- data.frame()
# Add xgboost algorithm
# Regression
if (glb_is_regression) {
glbMdlMethods <- c(NULL
# deterministic
#, "lm", # same as glm
, "glm", "bayesglm", "glmnet"
, "rpart"
# non-deterministic
, "gbm", "rf"
# Unknown
, "nnet" , "avNNet" # runs 25 models per cv sample for tunelength=5
, "svmLinear", "svmLinear2"
, "svmPoly" # runs 75 models per cv sample for tunelength=5
, "svmRadial"
, "earth"
, "bagEarth" # Takes a long time
)
} else
# Classification - Add ada (auto feature selection)
if (glb_is_binomial)
glbMdlMethods <- c(NULL
# deterministic
, "bagEarth" # Takes a long time
, "glm", "bayesglm", "glmnet"
, "nnet"
, "rpart"
# non-deterministic
, "gbm"
, "avNNet" # runs 25 models per cv sample for tunelength=5
, "rf"
# Unknown
, "lda", "lda2"
# svm models crash when predict is called -> internal to kernlab it should call predict without .outcome
, "svmLinear", "svmLinear2"
, "svmPoly" # runs 75 models per cv sample for tunelength=5
, "svmRadial"
, "earth"
) else
glbMdlMethods <- c(NULL
# deterministic
,"glmnet"
# non-deterministic
,"rf"
# Unknown
,"gbm","rpart"
)
glbMdlFamilies <- list(); glb_mdl_feats_lst <- list()
# family: Choose from c("RFE.X", "CSM.X", "All.X", "Best.Interact")
# methods: Choose from c(NULL, <method>, glbMdlMethods)
#glbMdlFamilies[["RFE.X"]] <- c("glmnet", "glm") # non-NULL vector is mandatory
if (glb_is_classification && !glb_is_binomial)
# glm does not work for multinomial
glbMdlFamilies[["All.X"]] <- c("glmnet") else
glbMdlFamilies[["All.X"]] <- c("glmnet", "glm")
#glbMdlFamilies[["Best.Interact"]] <- "glmnet" # non-NULL vector is mandatory
# Check if interaction features make RFE better
# glbMdlFamilies[["CSM.X"]] <- setdiff(glbMdlMethods, c("lda", "lda2")) # crashing due to category:.clusterid ??? #c("glmnet", "glm") # non-NULL list is mandatory
# glb_mdl_feats_lst[["CSM.X"]] <- c(NULL
# , <comma-separated-features-vector>
# )
# dAFeats.CSM.X %<d-% c(NULL
# # Interaction feats up to varImp(RFE.X.glmnet) >= 50
# , <comma-separated-features-vector>
# , setdiff(myextract_actual_feats(predictors(rfe_fit_results)), c(NULL
# , <comma-separated-features-vector>
# ))
# )
# glb_mdl_feats_lst[["CSM.X"]] <- "%<d-% dAFeats.CSM.X"
glbMdlFamilies[["Final"]] <- c(NULL) # NULL vector acceptable # c("glmnet", "glm")
glbMdlAllowParallel <- list()
#glbMdlAllowParallel[["Final##rcv#glmnet"]] <- FALSE
# Check if tuning parameters make fit better; make it mdlFamily customizable ?
glbMdlTuneParams <- data.frame()
# When glmnet crashes at model$grid with error: ???
glmnetTuneParams <- rbind(data.frame()
,data.frame(parameter = "alpha", vals = "0.100 0.325 0.550 0.775 1.000")
,data.frame(parameter = "lambda", vals = "9.342e-02")
)
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams,
# cbind(data.frame(mdlId = "<mdlId>"),
# glmnetTuneParams))
#avNNet
# size=[1] 3 5 7 9; decay=[0] 1e-04 0.001 0.01 0.1; bag=[FALSE]; RMSE=1.3300906
#bagEarth
# degree=1 [2] 3; nprune=64 128 256 512 [1024]; RMSE=0.6486663 (up)
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "bagEarth", parameter = "nprune", vals = "256")
# ,data.frame(method = "bagEarth", parameter = "degree", vals = "2")
# ))
#earth
# degree=[1]; nprune=2 [9] 17 25 33; RMSE=0.1334478
#gbm
# shrinkage=0.05 [0.10] 0.15 0.20 0.25; n.trees=100 150 200 [250] 300; interaction.depth=[1] 2 3 4 5; n.minobsinnode=[10]; RMSE=0.2008313
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "gbm", parameter = "shrinkage", min = 0.05, max = 0.25, by = 0.05)
# ,data.frame(method = "gbm", parameter = "n.trees", min = 100, max = 300, by = 50)
# ,data.frame(method = "gbm", parameter = "interaction.depth", min = 1, max = 5, by = 1)
# ,data.frame(method = "gbm", parameter = "n.minobsinnode", min = 10, max = 10, by = 10)
# #seq(from=0.05, to=0.25, by=0.05)
# ))
#glmnet
# alpha=0.100 [0.325] 0.550 0.775 1.000; lambda=0.0005232693 0.0024288010 0.0112734954 [0.0523269304] 0.2428800957; RMSE=0.6164891
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "glmnet", parameter = "alpha", vals = "0.550 0.775 0.8875 0.94375 1.000")
# ,data.frame(method = "glmnet", parameter = "lambda", vals = "9.858855e-05 0.0001971771 0.0009152152 0.0042480525 0.0197177130")
# ))
#nnet
# size=3 5 [7] 9 11; decay=0.0001 0.001 0.01 [0.1] 0.2; RMSE=0.9287422
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "nnet", parameter = "size", vals = "3 5 7 9 11")
# ,data.frame(method = "nnet", parameter = "decay", vals = "0.0001 0.0010 0.0100 0.1000 0.2000")
# ))
#rf # Don't bother; results are not deterministic
# mtry=2 35 68 [101] 134; RMSE=0.1339974
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "rf", parameter = "mtry", vals = "2 5 9 13 17")
# ))
#rpart
# cp=0.020 [0.025] 0.030 0.035 0.040; RMSE=0.1770237
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "rpart", parameter = "cp", vals = "0.004347826 0.008695652 0.017391304 0.021739130 0.034782609")
# ))
#svmLinear
# C=0.01 0.05 [0.10] 0.50 1.00 2.00 3.00 4.00; RMSE=0.1271318; 0.1296718
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "svmLinear", parameter = "C", vals = "0.01 0.05 0.1 0.5 1")
# ))
#svmLinear2
# cost=0.0625 0.1250 [0.25] 0.50 1.00; RMSE=0.1276354
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "svmLinear2", parameter = "cost", vals = "0.0625 0.125 0.25 0.5 1")
# ))
#svmPoly
# degree=[1] 2 3 4 5; scale=0.01 0.05 [0.1] 0.5 1; C=0.50 1.00 [2.00] 3.00 4.00; RMSE=0.1276130
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method="svmPoly", parameter="degree", min=1, max=5, by=1) #seq(1, 5, 1)
# ,data.frame(method="svmPoly", parameter="scale", vals="0.01, 0.05, 0.1, 0.5, 1")
# ,data.frame(method="svmPoly", parameter="C", vals="0.50, 1.00, 2.00, 3.00, 4.00")
# ))
#svmRadial
# sigma=[0.08674323]; C=0.25 0.50 1.00 [2.00] 4.00; RMSE=0.1614957
#glb2Sav(); all.equal(sav_models_df, glb_models_df)
glb_preproc_methods <- NULL
# c("YeoJohnson", "center.scale", "range", "pca", "ica", "spatialSign")
# Baseline prediction model feature(s)
glb_Baseline_mdl_var <- NULL # or c("<feat>")
glbMdlMetric_terms <- NULL # or matrix(c(
# 0,1,2,3,4,
# 2,0,1,2,3,
# 4,2,0,1,2,
# 6,4,2,0,1,
# 8,6,4,2,0
# ), byrow=TRUE, nrow=5)
glbMdlMetricSummary <- NULL # or "<metric_name>"
glbMdlMetricMaximize <- NULL # or FALSE (TRUE is not the default for both classification & regression)
glbMdlMetricSummaryFn <- NULL # or function(data, lev=NULL, model=NULL) {
# confusion_mtrx <- t(as.matrix(confusionMatrix(data$pred, data$obs)))
# #print(confusion_mtrx)
# #print(confusion_mtrx * glbMdlMetric_terms)
# metric <- sum(confusion_mtrx * glbMdlMetric_terms) / nrow(data)
# names(metric) <- glbMdlMetricSummary
# return(metric)
# }
glbMdlCheckRcv <- FALSE # Turn it on when needed; otherwise takes long time
glb_rcv_n_folds <- 3 # or NULL
glb_rcv_n_repeats <- 3 # or NULL
glb_clf_proba_threshold <- NULL # 0.5
# Model selection criteria
if (glb_is_regression)
glbMdlMetricsEval <- c("min.RMSE.OOB", "max.R.sq.OOB", "max.Adj.R.sq.fit", "min.RMSE.fit")
#glbMdlMetricsEval <- c("min.RMSE.fit", "max.R.sq.fit", "max.Adj.R.sq.fit")
if (glb_is_classification) {
if (glb_is_binomial)
glbMdlMetricsEval <-
c("max.Accuracy.OOB", "max.AUCROCR.OOB", "max.AUCpROC.OOB", "min.aic.fit", "max.Accuracy.fit") else
glbMdlMetricsEval <- c("max.Accuracy.OOB", "max.Kappa.OOB")
}
# select from NULL [no ensemble models], "auto" [all models better than MFO or Baseline], c(mdl_ids in glb_models_lst) [Typically top-rated models in auto]
glb_mdl_ensemble <- NULL
# "%<d-% setdiff(mygetEnsembleAutoMdlIds(), 'CSM.X.rf')"
# c(<comma-separated-mdlIds>
# )
# Only for classifications; for regressions remove "(.*)\\.prob" form the regex
# tmp_fitobs_df <- glbObsFit[, grep(paste0("^", gsub(".", "\\.", mygetPredictIds$value, fixed = TRUE), "CSM\\.X\\.(.*)\\.prob"), names(glbObsFit), value = TRUE)]; cor_mtrx <- cor(tmp_fitobs_df); cor_vctr <- sort(cor_mtrx[row.names(orderBy(~-Overall, varImp(glb_models_lst[["Ensemble.repeatedcv.glmnet"]])$imp))[1], ]); summary(cor_vctr); cor_vctr
#ntv.glm <- glm(reformulate(indepVar, glb_rsp_var), family = "binomial", data = glbObsFit)
#step.glm <- step(ntv.glm)
glb_sel_mdl_id <- "All.X##rcv#glmnet" #select from c(NULL, "All.X##rcv#glmnet", "RFE.X##rcv#glmnet", <mdlId>)
glb_fin_mdl_id <- NULL #select from c(NULL, glb_sel_mdl_id)
glb_dsp_cols <- c(".pos", glbFeatsId, glbFeatsCategory, glb_rsp_var
# List critical cols excl. above
)
# Output specs
# lclgetfltout_df <- function(obsout_df) {
# require(tidyr)
# obsout_df <- obsout_df %>%
# tidyr::separate("ImageId.x.y", c(".src", ".pos", "x", "y"),
# sep = "#", remove = TRUE, extra = "merge")
# # mnm prefix stands for max_n_mean
# mnmout_df <- obsout_df %>%
# dplyr::group_by(.pos) %>%
# #dplyr::top_n(1, Probability1) %>% # Score = 3.9426
# #dplyr::top_n(2, Probability1) %>% # Score = ???; weighted = 3.94254;
# #dplyr::top_n(3, Probability1) %>% # Score = 3.9418; weighted = 3.94169;
# dplyr::top_n(4, Probability1) %>% # Score = ???; weighted = 3.94149;
# #dplyr::top_n(5, Probability1) %>% # Score = 3.9421; weighted = 3.94178
#
# # dplyr::summarize(xMeanN = mean(as.numeric(x)), yMeanN = mean(as.numeric(y)))
# # dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), Probability1), yMeanN = mean(as.numeric(y)))
# # dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), c(Probability1, 0.2357323, 0.2336925)), yMeanN = mean(as.numeric(y)))
# # dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), c(Probability1)), yMeanN = mean(as.numeric(y)))
# dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), c(Probability1)),
# yMeanN = weighted.mean(as.numeric(y), c(Probability1)))
#
# maxout_df <- obsout_df %>%
# dplyr::group_by(.pos) %>%
# dplyr::summarize(maxProb1 = max(Probability1))
# fltout_df <- merge(maxout_df, obsout_df,
# by.x = c(".pos", "maxProb1"), by.y = c(".pos", "Probability1"),
# all.x = TRUE)
# fmnout_df <- merge(fltout_df, mnmout_df,
# by.x = c(".pos"), by.y = c(".pos"),
# all.x = TRUE)
# return(fmnout_df)
# }
glbObsOut <- list(NULL
# glbFeatsId will be the first output column, by default
,vars = list()
# ,mapFn = function(obsout_df) {
# }
)
#obsout_df <- savobsout_df
# glbObsOut$mapFn <- function(obsout_df) {
# txfout_df <- dplyr::select(obsout_df, -.pos.y) %>%
# dplyr::mutate(
# lunch = levels(glbObsTrn[, "lunch" ])[
# round(mean(as.numeric(glbObsTrn[, "lunch" ])), 0)],
# dinner = levels(glbObsTrn[, "dinner" ])[
# round(mean(as.numeric(glbObsTrn[, "dinner" ])), 0)],
# reserve = levels(glbObsTrn[, "reserve" ])[
# round(mean(as.numeric(glbObsTrn[, "reserve" ])), 0)],
# outdoor = levels(glbObsTrn[, "outdoor" ])[
# round(mean(as.numeric(glbObsTrn[, "outdoor" ])), 0)],
# expensive = levels(glbObsTrn[, "expensive"])[
# round(mean(as.numeric(glbObsTrn[, "expensive"])), 0)],
# liquor = levels(glbObsTrn[, "liquor" ])[
# round(mean(as.numeric(glbObsTrn[, "liquor" ])), 0)],
# table = levels(glbObsTrn[, "table" ])[
# round(mean(as.numeric(glbObsTrn[, "table" ])), 0)],
# classy = levels(glbObsTrn[, "classy" ])[
# round(mean(as.numeric(glbObsTrn[, "classy" ])), 0)],
# kids = levels(glbObsTrn[, "kids" ])[
# round(mean(as.numeric(glbObsTrn[, "kids" ])), 0)]
# )
#
# print("ObsNew output class tables:")
# print(sapply(c("lunch","dinner","reserve","outdoor",
# "expensive","liquor","table",
# "classy","kids"),
# function(feat) table(txfout_df[, feat], useNA = "ifany")))
#
# txfout_df <- txfout_df %>%
# dplyr::mutate(labels = "") %>%
# dplyr::mutate(labels =
# ifelse(lunch != "-1", paste(labels, lunch ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(dinner != "-1", paste(labels, dinner ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(reserve != "-1", paste(labels, reserve ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(outdoor != "-1", paste(labels, outdoor ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(expensive != "-1", paste(labels, expensive), labels)) %>%
# dplyr::mutate(labels =
# ifelse(liquor != "-1", paste(labels, liquor ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(table != "-1", paste(labels, table ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(classy != "-1", paste(labels, classy ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(kids != "-1", paste(labels, kids ), labels)) %>%
# dplyr::select(business_id, labels)
# return(txfout_df)
# }
#if (!is.null(glbObsOut$mapFn)) obsout_df <- glbObsOut$mapFn(obsout_df); print(head(obsout_df))
glb_out_obs <- NULL # select from c(NULL : default to "new", "all", "new", "trn")
if (glb_is_classification && glb_is_binomial) {
glbObsOut$vars[["Probability1"]] <-
"%<d-% glbObsNew[, mygetPredictIds(glb_rsp_var, glb_fin_mdl_id)$prob]"
# glbObsOut$vars[[glb_rsp_var_raw]] <-
# "%<d-% glb_map_rsp_var_to_raw(glbObsNew[,
# mygetPredictIds(glb_rsp_var, glb_fin_mdl_id)$value])"
} else {
# glbObsOut$vars[[glbFeatsId]] <-
# "%<d-% as.integer(gsub('Test#', '', glbObsNew[, glbFeatsId]))"
glbObsOut$vars[[glb_rsp_var]] <-
"%<d-% glbObsNew[, mygetPredictIds(glb_rsp_var, glb_fin_mdl_id)$value]"
# for (outVar in setdiff(glbFeatsExcludeLcl, glb_rsp_var_raw))
# glbObsOut$vars[[outVar]] <-
# paste0("%<d-% mean(glbObsAll[, \"", outVar, "\"], na.rm = TRUE)")
}
# glbObsOut$vars[[glb_rsp_var_raw]] <- glb_rsp_var_raw
# glbObsOut$vars[[paste0(head(unlist(strsplit(mygetPredictIds$value, "")), -1), collapse = "")]] <-
glbOutStackFnames <- NULL #: default
# c("ebayipads_txt_assoc1_out_bid1_stack.csv") # manual stack
# c("ebayipads_finmdl_bid1_out_nnet_1.csv") # universal stack
glbOut <- list(pfx = "Pew_Anonymity_2016_")
# lclImageSampleSeed <- 129
glbOutDataVizFname <- NULL # choose from c(NULL, "<projectId>_obsall.csv")
glbChunks <- list(labels = c("set_global_options_wd","set_global_options"
,"import.data","inspect.data","scrub.data","transform.data"
,"extract.features"
,"extract.features.datetime","extract.features.image","extract.features.price"
,"extract.features.text","extract.features.string"
,"extract.features.end"
,"manage.missing.data","cluster.data","partition.data.training","select.features"
,"fit.models_0","fit.models_1","fit.models_2","fit.models_3"
,"fit.data.training_0","fit.data.training_1"
,"predict.data.new"
,"display.session.info"))
# To ensure that all chunks in this script are in glbChunks
if (!is.null(chkChunksLabels <- knitr::all_labels()) && # knitr::all_labels() doesn't work in console runs
!identical(chkChunksLabels, glbChunks$labels)) {
print(sprintf("setdiff(chkChunksLabels, glbChunks$labels): %s",
setdiff(chkChunksLabels, glbChunks$labels)))
print(sprintf("setdiff(glbChunks$labels, chkChunksLabels): %s",
setdiff(glbChunks$labels, chkChunksLabels)))
}
glbChunks[["first"]] <- NULL #default: script will load envir from previous chunk
glbChunks[["last"]] <- NULL #"extract.features.end" #NULL #default: script will save envir at end of this chunk
#mysavChunk(glbOut$pfx, glbChunks[["last"]])
# Inspect max OOB FP
#chkObsOOB <- subset(glbObsOOB, !label.fctr.All.X..rcv.glmnet.is.acc)
#chkObsOOBFP <- subset(chkObsOOB, label.fctr.All.X..rcv.glmnet == "left_eye_center") %>% dplyr::mutate(Probability1 = label.fctr.All.X..rcv.glmnet.prob) %>% select(-.src, -.pos, -x, -y) %>% lclgetfltout_df() %>% mutate(obj.distance = (((as.numeric(x) - left_eye_center_x.int) ^ 2) + ((as.numeric(y) - left_eye_center_y.int) ^ 2)) ^ 0.5) %>% dplyr::top_n(5, obj.distance) %>% dplyr::top_n(5, -patch.cor)
#
#newImgObs <- glbObsNew[(glbObsNew$ImageId == "Test#0001"), ]; print(newImgObs[which.max(newImgObs$label.fctr.Final..rcv.glmnet.prob), ])
#OOBImgObs <- glbObsOOB[(glbObsOOB$ImageId == "Train#0003"), ]; print(OOBImgObs[which.max(OOBImgObs$label.fctr.All.X..rcv.glmnet.prob), ])
#load("Pew_Anonymity_2016_extract.features.end.RData", verbose = TRUE)
#mygetImage(which(glbObsAll[, glbFeatsId] == "Train#0003"), names(glbFeatsImage)[1], plot = TRUE, featHighlight = c("left_eye_center_x", "left_eye_center_y"), ovrlHighlight = c(66, 35))
# Depict process
glb_analytics_pn <- petrinet(name = "glb_analytics_pn",
trans_df = data.frame(id = 1:6,
name = c("data.training.all","data.new",
"model.selected","model.final",
"data.training.all.prediction","data.new.prediction"),
x=c( -5,-5,-15,-25,-25,-35),
y=c( -5, 5, 0, 0, -5, 5)
),
places_df=data.frame(id=1:4,
name=c("bgn","fit.data.training.all","predict.data.new","end"),
x=c( -0, -20, -30, -40),
y=c( 0, 0, 0, 0),
M0=c( 3, 0, 0, 0)
),
arcs_df = data.frame(
begin = c("bgn","bgn","bgn",
"data.training.all","model.selected","fit.data.training.all",
"fit.data.training.all","model.final",
"data.new","predict.data.new",
"data.training.all.prediction","data.new.prediction"),
end = c("data.training.all","data.new","model.selected",
"fit.data.training.all","fit.data.training.all","model.final",
"data.training.all.prediction","predict.data.new",
"predict.data.new","data.new.prediction",
"end","end")
))
#print(ggplot.petrinet(glb_analytics_pn))
print(ggplot.petrinet(glb_analytics_pn) + coord_flip())## Loading required package: grid
glb_analytics_avl_objs <- NULL
glb_chunks_df <- myadd_chunk(NULL, "import.data")## label step_major step_minor label_minor bgn end elapsed
## 1 import.data 1 0 0 13.607 NA NAStep 1.0: import data
chunk option: eval=
## [1] "Reading file ./data/AnonymityPoll.csv..."
## [1] "dimensions of data in ./data/AnonymityPoll.csv: 1,002 rows x 13 cols"
## Internet.Use Smartphone Sex Age State Region
## 1 1 0 Male 62 Massachusetts Northeast
## 2 1 0 Male 45 South Carolina South
## 3 0 1 Female 70 New Jersey Northeast
## 4 1 0 Male 70 Georgia South
## 5 0 NA Female 80 Georgia South
## 6 1 1 Male 49 Tennessee South
## Conservativeness Info.On.Internet Worry.About.Info Privacy.Importance
## 1 4 0 1 100.00000
## 2 1 1 0 0.00000
## 3 4 0 0 NA
## 4 4 3 1 88.88889
## 5 4 NA NA NA
## 6 4 6 0 88.88889
## Anonymity.Possible Tried.Masking.Identity Privacy.Laws.Effective
## 1 0 0 0
## 2 1 0 1
## 3 0 0 NA
## 4 1 0 0
## 5 NA NA NA
## 6 1 1 0
## Internet.Use Smartphone Sex Age State Region
## 35 1 0 Female 74 Florida South
## 153 0 0 Female 77 Oregon West
## 511 1 1 Male 19 Virginia South
## 729 0 1 Male 52 Connecticut Northeast
## 734 1 1 Male 26 Wisconsin Midwest
## 990 1 1 Female 36 Missouri Midwest
## Conservativeness Info.On.Internet Worry.About.Info Privacy.Importance
## 35 3 0 0 6.25
## 153 3 NA NA NA
## 511 3 7 0 100.00
## 729 2 1 0 50.00
## 734 5 2 0 100.00
## 990 3 6 0 100.00
## Anonymity.Possible Tried.Masking.Identity Privacy.Laws.Effective
## 35 0 0 0
## 153 NA NA NA
## 511 1 0 0
## 729 1 0 1
## 734 0 0 0
## 990 0 0 1
## Internet.Use Smartphone Sex Age State Region Conservativeness
## 997 1 1 Male 29 California West 3
## 998 1 1 Female 57 Utah West 4
## 999 0 NA Male 29 Colorado West 3
## 1000 1 1 Male 22 California West 4
## 1001 0 0 Female 63 California West 4
## 1002 1 1 Female 26 Texas South 3
## Info.On.Internet Worry.About.Info Privacy.Importance
## 997 7 1 77.77778
## 998 7 1 27.77778
## 999 NA NA NA
## 1000 6 0 11.11111
## 1001 NA NA NA
## 1002 3 1 55.55556
## Anonymity.Possible Tried.Masking.Identity Privacy.Laws.Effective
## 997 1 1 1
## 998 0 0 1
## 999 NA NA 0
## 1000 0 0 1
## 1001 NA NA 1
## 1002 0 0 0
## 'data.frame': 1002 obs. of 13 variables:
## $ Internet.Use : int 1 1 0 1 0 1 1 0 0 1 ...
## $ Smartphone : int 0 0 1 0 NA 1 0 0 NA 0 ...
## $ Sex : chr "Male" "Male" "Female" "Male" ...
## $ Age : int 62 45 70 70 80 49 52 76 75 76 ...
## $ State : chr "Massachusetts" "South Carolina" "New Jersey" "Georgia" ...
## $ Region : chr "Northeast" "South" "Northeast" "South" ...
## $ Conservativeness : int 4 1 4 4 4 4 3 3 4 4 ...
## $ Info.On.Internet : int 0 1 0 3 NA 6 3 NA NA 0 ...
## $ Worry.About.Info : int 1 0 0 1 NA 0 1 NA NA 0 ...
## $ Privacy.Importance : num 100 0 NA 88.9 NA ...
## $ Anonymity.Possible : int 0 1 0 1 NA 1 0 NA NA 1 ...
## $ Tried.Masking.Identity: int 0 0 0 0 NA 1 0 NA NA 0 ...
## $ Privacy.Laws.Effective: int 0 1 NA 0 NA 0 1 NA 0 1 ...
## - attr(*, "comment")= chr "glbObsTrn"
## NULL## Internet.Use Smartphone Sex Age State Region
## 3 0 1 Female 70 New Jersey Northeast
## 5 0 NA Female 80 Georgia South
## 8 0 0 Female 76 New York Northeast
## 9 0 NA Male 75 North Carolina South
## 11 0 0 Male 69 Ohio Midwest
## 13 0 0 Male 72 New York Northeast
## Conservativeness Info.On.Internet Worry.About.Info Privacy.Importance
## 3 4 0 0 NA
## 5 4 NA NA NA
## 8 3 NA NA NA
## 9 4 NA NA NA
## 11 4 NA NA NA
## 13 5 NA NA NA
## Anonymity.Possible Tried.Masking.Identity Privacy.Laws.Effective
## 3 0 0 NA
## 5 NA NA NA
## 8 NA NA NA
## 9 NA NA 0
## 11 NA NA 0
## 13 NA NA 1
## Internet.Use Smartphone Sex Age State Region
## 3 0 1 Female 70 New Jersey Northeast
## 113 0 0 Female 24 Tennessee South
## 231 0 0 Male 60 Missouri Midwest
## 234 0 0 Female 76 Georgia South
## 243 0 0 Female 65 Louisiana South
## 299 0 0 Female 94 Wisconsin Midwest
## Conservativeness Info.On.Internet Worry.About.Info Privacy.Importance
## 3 4 0 0 NA
## 113 2 NA NA NA
## 231 2 NA NA NA
## 234 4 NA NA NA
## 243 4 NA NA NA
## 299 3 NA NA NA
## Anonymity.Possible Tried.Masking.Identity Privacy.Laws.Effective
## 3 0 0 NA
## 113 NA NA 1
## 231 NA NA 0
## 234 NA NA 1
## 243 NA NA NA
## 299 NA NA 0
## Internet.Use Smartphone Sex Age State Region
## 960 0 0 Female 39 Texas South
## 965 0 0 Female 70 New York Northeast
## 974 0 NA Male 52 Ohio Midwest
## 984 0 0 Male 84 Arkansas South
## 999 0 NA Male 29 Colorado West
## 1001 0 0 Female 63 California West
## Conservativeness Info.On.Internet Worry.About.Info Privacy.Importance
## 960 3 NA NA NA
## 965 4 NA NA NA
## 974 2 NA NA NA
## 984 4 NA NA NA
## 999 3 NA NA NA
## 1001 4 NA NA NA
## Anonymity.Possible Tried.Masking.Identity Privacy.Laws.Effective
## 960 NA NA 1
## 965 NA NA 0
## 974 NA NA 0
## 984 NA NA NA
## 999 NA NA 0
## 1001 NA NA 1
## 'data.frame': 215 obs. of 13 variables:
## $ Internet.Use : int 0 0 0 0 0 0 0 0 0 0 ...
## $ Smartphone : int 1 NA 0 NA 0 0 0 0 NA 0 ...
## $ Sex : chr "Female" "Female" "Female" "Male" ...
## $ Age : int 70 80 76 75 69 72 63 63 80 73 ...
## $ State : chr "New Jersey" "Georgia" "New York" "North Carolina" ...
## $ Region : chr "Northeast" "South" "Northeast" "South" ...
## $ Conservativeness : int 4 4 3 4 4 5 3 3 5 NA ...
## $ Info.On.Internet : int 0 NA NA NA NA NA NA NA NA NA ...
## $ Worry.About.Info : int 0 NA NA NA NA NA NA NA NA NA ...
## $ Privacy.Importance : num NA NA NA NA NA NA NA NA NA NA ...
## $ Anonymity.Possible : int 0 NA NA NA NA NA NA NA NA NA ...
## $ Tried.Masking.Identity: int 0 NA NA NA NA NA NA NA NA NA ...
## $ Privacy.Laws.Effective: int NA NA NA 0 0 1 0 0 NA 0 ...
## - attr(*, "comment")= chr "glbObsNew"
## Internet.Use Smartphone Sex Age State Region
## 1 1 0 Male 62 Massachusetts Northeast
## 2 1 0 Male 45 South Carolina South
## 4 1 0 Male 70 Georgia South
## 6 1 1 Male 49 Tennessee South
## 7 1 0 Female 52 Michigan Midwest
## 10 1 0 Female 76 North Carolina South
## Conservativeness Info.On.Internet Worry.About.Info Privacy.Importance
## 1 4 0 1 100.00000
## 2 1 1 0 0.00000
## 4 4 3 1 88.88889
## 6 4 6 0 88.88889
## 7 3 3 1 33.33333
## 10 4 0 0 56.25000
## Anonymity.Possible Tried.Masking.Identity Privacy.Laws.Effective
## 1 0 0 0
## 2 1 0 1
## 4 1 0 0
## 6 1 1 0
## 7 0 0 1
## 10 1 0 1
## Internet.Use Smartphone Sex Age State Region
## 376 1 0 Female 64 Washington West
## 498 1 1 Male 33 New York Northeast
## 676 1 1 Male 37 Georgia South
## 735 1 1 Male 37 Mississippi South
## 958 1 1 Male 48 New York Northeast
## 968 1 1 Male 23 Virginia South
## Conservativeness Info.On.Internet Worry.About.Info Privacy.Importance
## 376 4 2 1 77.777778
## 498 3 3 1 100.000000
## 676 NA 4 1 77.777778
## 735 3 2 1 44.444444
## 958 4 6 1 61.111111
## 968 NA 3 0 5.555556
## Anonymity.Possible Tried.Masking.Identity Privacy.Laws.Effective
## 376 0 0 0
## 498 0 0 0
## 676 0 0 0
## 735 NA 0 0
## 958 0 0 0
## 968 1 0 1
## Internet.Use Smartphone Sex Age State Region Conservativeness
## 995 1 1 Female 55 Colorado West 3
## 996 1 1 Female 30 Arizona West 2
## 997 1 1 Male 29 California West 3
## 998 1 1 Female 57 Utah West 4
## 1000 1 1 Male 22 California West 4
## 1002 1 1 Female 26 Texas South 3
## Info.On.Internet Worry.About.Info Privacy.Importance
## 995 3 1 88.88889
## 996 5 0 94.44444
## 997 7 1 77.77778
## 998 7 1 27.77778
## 1000 6 0 11.11111
## 1002 3 1 55.55556
## Anonymity.Possible Tried.Masking.Identity Privacy.Laws.Effective
## 995 0 0 0
## 996 1 0 0
## 997 1 1 1
## 998 0 0 1
## 1000 0 0 1
## 1002 0 0 0
## 'data.frame': 787 obs. of 13 variables:
## $ Internet.Use : int 1 1 1 1 1 1 1 1 1 1 ...
## $ Smartphone : int 0 0 0 1 0 0 1 1 0 0 ...
## $ Sex : chr "Male" "Male" "Male" "Male" ...
## $ Age : int 62 45 70 49 52 76 50 47 69 41 ...
## $ State : chr "Massachusetts" "South Carolina" "Georgia" "Tennessee" ...
## $ Region : chr "Northeast" "South" "South" "South" ...
## $ Conservativeness : int 4 1 4 4 3 4 3 3 3 NA ...
## $ Info.On.Internet : int 0 1 3 6 3 0 1 0 9 0 ...
## $ Worry.About.Info : int 1 0 1 0 1 0 0 0 0 1 ...
## $ Privacy.Importance : num 100 0 88.9 88.9 33.3 ...
## $ Anonymity.Possible : int 0 1 1 1 0 1 0 1 0 NA ...
## $ Tried.Masking.Identity: int 0 0 0 1 0 0 0 0 0 0 ...
## $ Privacy.Laws.Effective: int 0 1 0 0 1 1 0 0 0 0 ...## [1] "Creating new feature: .pos..."
## [1] "Creating new feature: .pos.y..."## Warning: using .rownames as identifiers for observations## [1] "Partition stats:"## Loading required package: sqldf## Loading required package: gsubfn## Loading required package: proto## Loading required package: RSQLite## Loading required package: DBI## Loading required package: tcltk## Privacy.Importance.cut.fctr .src .n
## 1 (66.7,100] Train 428
## 2 <NA> Test 215
## 3 (-0.1,33.3] Train 181
## 4 (33.3,66.7] Train 178
## Privacy.Importance.cut.fctr .src .n
## 1 (66.7,100] Train 428
## 2 <NA> Test 215
## 3 (-0.1,33.3] Train 181
## 4 (33.3,66.7] Train 178## Loading required package: RColorBrewer
## .src .n
## 1 Train 787
## 2 Test 215## Loading required package: lazyeval## Loading required package: gdata## gdata: read.xls support for 'XLS' (Excel 97-2004) files ENABLED.## ## gdata: read.xls support for 'XLSX' (Excel 2007+) files ENABLED.##
## Attaching package: 'gdata'## The following objects are masked from 'package:dplyr':
##
## combine, first, last## The following object is masked from 'package:stats':
##
## nobs## The following object is masked from 'package:utils':
##
## object.size## [1] "Found 0 duplicates by all features:"## NULL## label step_major step_minor label_minor bgn end elapsed
## 1 import.data 1 0 0 13.607 25.481 11.874
## 2 inspect.data 2 0 0 25.482 NA NAStep 2.0: inspect data
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 215 rows containing non-finite values (stat_bin).
## [1] "numeric data missing in glbObsAll: "
## Internet.Use Smartphone Age
## 1 43 27
## Conservativeness Info.On.Internet Worry.About.Info
## 62 210 212
## Privacy.Importance Anonymity.Possible Tried.Masking.Identity
## 215 249 218
## Privacy.Laws.Effective
## 108
## [1] "numeric data w/ 0s in glbObsAll: "
## Internet.Use Smartphone Info.On.Internet
## 226 472 105
## Worry.About.Info Privacy.Importance Anonymity.Possible
## 404 43 475
## Tried.Masking.Identity Privacy.Laws.Effective
## 656 660
## [1] "numeric data w/ Infs in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ NaNs in glbObsAll: "
## named integer(0)
## [1] "string data missing in glbObsAll: "
## Sex State Region
## 0 0 0
## [1] "elapsed Time (secs): 2.510000"## Warning: Removed 215 rows containing non-finite values (stat_smooth).## Warning: Computation failed in `stat_smooth()`:
## x has insufficient unique values to support 10 knots: reduce k.## Warning: Removed 215 rows containing non-finite values (stat_smooth).## Warning: Removed 215 rows containing missing values (geom_point).## Warning: Removed 235 rows containing non-finite values (stat_smooth).## Warning: Computation failed in `stat_smooth()`:
## x has insufficient unique values to support 10 knots: reduce k.## Warning: Removed 235 rows containing non-finite values (stat_smooth).## Warning: Removed 235 rows containing missing values (geom_point).## Warning: Removed 237 rows containing non-finite values (stat_smooth).
## Warning: Removed 237 rows containing non-finite values (stat_smooth).## Warning: Removed 237 rows containing missing values (geom_point).## Warning: Removed 259 rows containing non-finite values (stat_smooth).## Warning: Computation failed in `stat_smooth()`:
## x has insufficient unique values to support 10 knots: reduce k.## Warning: Removed 259 rows containing non-finite values (stat_smooth).## Warning: Removed 259 rows containing missing values (geom_point).
## Warning: Removed 215 rows containing non-finite values (stat_smooth).## Warning: Removed 215 rows containing non-finite values (stat_smooth).## Warning: Removed 215 rows containing missing values (geom_point).## Warning: Removed 217 rows containing non-finite values (stat_smooth).## Warning: Computation failed in `stat_smooth()`:
## x has insufficient unique values to support 10 knots: reduce k.## Warning: Removed 217 rows containing non-finite values (stat_smooth).## Warning: Removed 217 rows containing missing values (geom_point).## Warning: Removed 254 rows containing non-finite values (stat_smooth).## Warning: Computation failed in `stat_smooth()`:
## x has insufficient unique values to support 10 knots: reduce k.## Warning: Removed 254 rows containing non-finite values (stat_smooth).## Warning: Removed 254 rows containing missing values (geom_point).## Warning: Removed 223 rows containing non-finite values (stat_smooth).## Warning: Computation failed in `stat_smooth()`:
## x has insufficient unique values to support 10 knots: reduce k.## Warning: Removed 223 rows containing non-finite values (stat_smooth).## Warning: Removed 223 rows containing missing values (geom_point).
## Warning: Removed 279 rows containing non-finite values (stat_smooth).## Warning: Computation failed in `stat_smooth()`:
## x has insufficient unique values to support 10 knots: reduce k.## Warning: Removed 279 rows containing non-finite values (stat_smooth).## Warning: Removed 279 rows containing missing values (geom_point).## Warning: Removed 215 rows containing non-finite values (stat_smooth).
## Warning: Removed 215 rows containing non-finite values (stat_smooth).## Warning: Removed 215 rows containing missing values (geom_point).## Warning: Removed 215 rows containing non-finite values (stat_smooth).
## Warning: Removed 215 rows containing non-finite values (stat_smooth).## Warning: Removed 215 rows containing missing values (geom_point).## Warning: Removed 215 rows containing non-finite values (stat_smooth).
## Warning: Removed 215 rows containing non-finite values (stat_smooth).## Warning: Removed 215 rows containing missing values (geom_point).
## Warning: Removed 215 rows containing non-finite values (stat_smooth).## Warning: Removed 215 rows containing non-finite values (stat_smooth).## Warning: Removed 215 rows containing missing values (geom_point).
## [1] "elapsed Time (secs): 12.683000"
## [1] "elapsed Time (secs): 12.683000"## label step_major step_minor label_minor bgn end elapsed
## 2 inspect.data 2 0 0 25.482 42.118 16.636
## 3 scrub.data 2 1 1 42.119 NA NAStep 2.1: scrub data
## [1] "numeric data missing in glbObsAll: "
## Internet.Use Smartphone Age
## 1 43 27
## Conservativeness Info.On.Internet Worry.About.Info
## 62 210 212
## Privacy.Importance Anonymity.Possible Tried.Masking.Identity
## 215 249 218
## Privacy.Laws.Effective
## 108
## [1] "numeric data w/ 0s in glbObsAll: "
## Internet.Use Smartphone Info.On.Internet
## 226 472 105
## Worry.About.Info Privacy.Importance Anonymity.Possible
## 404 43 475
## Tried.Masking.Identity Privacy.Laws.Effective
## 656 660
## [1] "numeric data w/ Infs in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ NaNs in glbObsAll: "
## named integer(0)
## [1] "string data missing in glbObsAll: "
## Sex State Region
## 0 0 0## label step_major step_minor label_minor bgn end elapsed
## 3 scrub.data 2 1 1 42.119 51.824 9.705
## 4 transform.data 2 2 2 51.825 NA NAStep 2.2: transform data
## Warning in myderiveFeatures(glbObsAll, glbFeatsDerive): .pos already
## present in glbObsAll, skipping ...## Warning in myderiveFeatures(glbObsAll, glbFeatsDerive): .pos.y already
## present in glbObsAll, skipping ...## label step_major step_minor label_minor bgn end elapsed
## 4 transform.data 2 2 2 51.825 51.869 0.045
## 5 extract.features 3 0 0 51.870 NA NAStep 3.0: extract features
## label step_major step_minor label_minor bgn
## 5 extract.features 3 0 0 51.870
## 6 extract.features.datetime 3 1 1 51.907
## end elapsed
## 5 51.906 0.036
## 6 NA NAStep 3.1: extract features datetime
## label step_major step_minor label_minor bgn
## 1 extract.features.datetime.bgn 1 0 0 51.936
## end elapsed
## 1 NA NA## label step_major step_minor label_minor bgn
## 6 extract.features.datetime 3 1 1 51.907
## 7 extract.features.image 3 2 2 51.946
## end elapsed
## 6 51.946 0.039
## 7 NA NAStep 3.2: extract features image
```{r extract.features.image, cache=FALSE, echo=FALSE, fig.height=5, fig.width=5, eval=myevlChunk(glbChunks, glbOut$pfx)}
## label step_major step_minor label_minor bgn end
## 1 extract.features.image.bgn 1 0 0 51.98 NA
## elapsed
## 1 NA## label step_major step_minor label_minor bgn
## 1 extract.features.image.bgn 1 0 0 51.980
## 2 extract.features.image.end 2 0 0 51.989
## end elapsed
## 1 51.989 0.009
## 2 NA NA## label step_major step_minor label_minor bgn
## 1 extract.features.image.bgn 1 0 0 51.980
## 2 extract.features.image.end 2 0 0 51.989
## end elapsed
## 1 51.989 0.009
## 2 NA NA## label step_major step_minor label_minor bgn end
## 7 extract.features.image 3 2 2 51.946 51.999
## 8 extract.features.price 3 3 3 52.000 NA
## elapsed
## 7 0.053
## 8 NAStep 3.3: extract features price
## label step_major step_minor label_minor bgn end
## 1 extract.features.price.bgn 1 0 0 52.026 NA
## elapsed
## 1 NA## label step_major step_minor label_minor bgn end
## 8 extract.features.price 3 3 3 52.000 52.036
## 9 extract.features.text 3 4 4 52.036 NA
## elapsed
## 8 0.036
## 9 NAStep 3.4: extract features text
## label step_major step_minor label_minor bgn end
## 1 extract.features.text.bgn 1 0 0 52.077 NA
## elapsed
## 1 NA## label step_major step_minor label_minor bgn end
## 9 extract.features.text 3 4 4 52.036 52.086
## 10 extract.features.string 3 5 5 52.087 NA
## elapsed
## 9 0.05
## 10 NAStep 3.5: extract features string
## label step_major step_minor label_minor bgn end
## 1 extract.features.string.bgn 1 0 0 52.12 NA
## elapsed
## 1 NA## label step_major step_minor
## 1 extract.features.string.bgn 1 0
## 2 extract.features.stringfactorize.str.vars 2 0
## label_minor bgn end elapsed
## 1 0 52.120 52.131 0.011
## 2 0 52.132 NA NA## Sex State Region .src
## "Sex" "State" "Region" ".src"## Warning: Creating factors of string variable: Sex: # of unique values: 2## Warning: Creating factors of string variable: Region: # of unique values: 4## label step_major step_minor label_minor bgn end
## 10 extract.features.string 3 5 5 52.087 52.148
## 11 extract.features.end 3 6 6 52.149 NA
## elapsed
## 10 0.061
## 11 NAStep 3.6: extract features end
## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
## label step_major step_minor label_minor bgn end
## 11 extract.features.end 3 6 6 52.149 53.07
## 12 manage.missing.data 4 0 0 53.071 NA
## elapsed
## 11 0.922
## 12 NAStep 4.0: manage missing data
## [1] "numeric data missing in glbObsAll: "
## Internet.Use Smartphone Age
## 1 43 27
## Conservativeness Info.On.Internet Worry.About.Info
## 62 210 212
## Privacy.Importance Anonymity.Possible Tried.Masking.Identity
## 215 249 218
## Privacy.Laws.Effective
## 108
## [1] "numeric data w/ 0s in glbObsAll: "
## Internet.Use Smartphone Info.On.Internet
## 226 472 105
## Worry.About.Info Privacy.Importance Anonymity.Possible
## 404 43 475
## Tried.Masking.Identity Privacy.Laws.Effective
## 656 660
## [1] "numeric data w/ Infs in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ NaNs in glbObsAll: "
## named integer(0)
## [1] "string data missing in glbObsAll: "
## Sex State Region
## 0 0 0## [1] "Missing data for numerics:"
## Internet.Use Smartphone Age
## 1 43 27
## Conservativeness Info.On.Internet Worry.About.Info
## 62 210 212
## Privacy.Importance Anonymity.Possible Tried.Masking.Identity
## 215 249 218
## Privacy.Laws.Effective
## 108## Loading required package: mice## Loading required package: Rcpp## mice 2.25 2015-11-09## [1] "Summary before imputation: "
## Internet.Use Smartphone Age Conservativeness
## Min. :0.0000 Min. :0.0000 Min. :18.00 Min. :1.000
## 1st Qu.:1.0000 1st Qu.:0.0000 1st Qu.:37.00 1st Qu.:3.000
## Median :1.0000 Median :1.0000 Median :55.00 Median :3.000
## Mean :0.7742 Mean :0.5078 Mean :52.37 Mean :3.277
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:66.00 3rd Qu.:4.000
## Max. :1.0000 Max. :1.0000 Max. :96.00 Max. :5.000
## NA's :1 NA's :43 NA's :27 NA's :62
## Info.On.Internet Worry.About.Info Anonymity.Possible
## Min. : 0.000 Min. :0.0000 Min. :0.0000
## 1st Qu.: 2.000 1st Qu.:0.0000 1st Qu.:0.0000
## Median : 4.000 Median :0.0000 Median :0.0000
## Mean : 3.795 Mean :0.4886 Mean :0.3692
## 3rd Qu.: 6.000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :11.000 Max. :1.0000 Max. :1.0000
## NA's :210 NA's :212 NA's :249
## Tried.Masking.Identity Privacy.Laws.Effective Sex.fctr
## Min. :0.0000 Min. :0.0000 Female:505
## 1st Qu.:0.0000 1st Qu.:0.0000 Male :497
## Median :0.0000 Median :0.0000
## Mean :0.1633 Mean :0.2617
## 3rd Qu.:0.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000
## NA's :218 NA's :108
## Region.fctr
## South :359
## Midwest :239
## Northeast:166
## West :238
##
##
##
##
## iter imp variable
## 1 1 Internet.Use Smartphone Age Conservativeness Info.On.Internet Worry.About.Info Anonymity.Possible Tried.Masking.Identity Privacy.Laws.Effective
## 1 2 Internet.Use Smartphone Age Conservativeness Info.On.Internet Worry.About.Info Anonymity.Possible Tried.Masking.Identity Privacy.Laws.Effective
## 1 3 Internet.Use Smartphone Age Conservativeness Info.On.Internet Worry.About.Info Anonymity.Possible Tried.Masking.Identity Privacy.Laws.Effective
## 1 4 Internet.Use Smartphone Age Conservativeness Info.On.Internet Worry.About.Info Anonymity.Possible Tried.Masking.Identity Privacy.Laws.Effective
## 1 5 Internet.Use Smartphone Age Conservativeness Info.On.Internet Worry.About.Info Anonymity.Possible Tried.Masking.Identity Privacy.Laws.Effective
## 2 1 Internet.Use Smartphone Age Conservativeness Info.On.Internet Worry.About.Info Anonymity.Possible Tried.Masking.Identity Privacy.Laws.Effective
## 2 2 Internet.Use Smartphone Age Conservativeness Info.On.Internet Worry.About.Info Anonymity.Possible Tried.Masking.Identity Privacy.Laws.Effective
## 2 3 Internet.Use Smartphone Age Conservativeness Info.On.Internet Worry.About.Info Anonymity.Possible Tried.Masking.Identity Privacy.Laws.Effective
## 2 4 Internet.Use Smartphone Age Conservativeness Info.On.Internet Worry.About.Info Anonymity.Possible Tried.Masking.Identity Privacy.Laws.Effective
## 2 5 Internet.Use Smartphone Age Conservativeness Info.On.Internet Worry.About.Info Anonymity.Possible Tried.Masking.Identity Privacy.Laws.Effective
## 3 1 Internet.Use Smartphone Age Conservativeness Info.On.Internet Worry.About.Info Anonymity.Possible Tried.Masking.Identity Privacy.Laws.Effective
## 3 2 Internet.Use Smartphone Age Conservativeness Info.On.Internet Worry.About.Info Anonymity.Possible Tried.Masking.Identity Privacy.Laws.Effective
## 3 3 Internet.Use Smartphone Age Conservativeness Info.On.Internet Worry.About.Info Anonymity.Possible Tried.Masking.Identity Privacy.Laws.Effective
## 3 4 Internet.Use Smartphone Age Conservativeness Info.On.Internet Worry.About.Info Anonymity.Possible Tried.Masking.Identity Privacy.Laws.Effective
## 3 5 Internet.Use Smartphone Age Conservativeness Info.On.Internet Worry.About.Info Anonymity.Possible Tried.Masking.Identity Privacy.Laws.Effective
## 4 1 Internet.Use Smartphone Age Conservativeness Info.On.Internet Worry.About.Info Anonymity.Possible Tried.Masking.Identity Privacy.Laws.Effective
## 4 2 Internet.Use Smartphone Age Conservativeness Info.On.Internet Worry.About.Info Anonymity.Possible Tried.Masking.Identity Privacy.Laws.Effective
## 4 3 Internet.Use Smartphone Age Conservativeness Info.On.Internet Worry.About.Info Anonymity.Possible Tried.Masking.Identity Privacy.Laws.Effective
## 4 4 Internet.Use Smartphone Age Conservativeness Info.On.Internet Worry.About.Info Anonymity.Possible Tried.Masking.Identity Privacy.Laws.Effective
## 4 5 Internet.Use Smartphone Age Conservativeness Info.On.Internet Worry.About.Info Anonymity.Possible Tried.Masking.Identity Privacy.Laws.Effective
## 5 1 Internet.Use Smartphone Age Conservativeness Info.On.Internet Worry.About.Info Anonymity.Possible Tried.Masking.Identity Privacy.Laws.Effective
## 5 2 Internet.Use Smartphone Age Conservativeness Info.On.Internet Worry.About.Info Anonymity.Possible Tried.Masking.Identity Privacy.Laws.Effective
## 5 3 Internet.Use Smartphone Age Conservativeness Info.On.Internet Worry.About.Info Anonymity.Possible Tried.Masking.Identity Privacy.Laws.Effective
## 5 4 Internet.Use Smartphone Age Conservativeness Info.On.Internet Worry.About.Info Anonymity.Possible Tried.Masking.Identity Privacy.Laws.Effective
## 5 5 Internet.Use Smartphone Age Conservativeness Info.On.Internet Worry.About.Info Anonymity.Possible Tried.Masking.Identity Privacy.Laws.Effective
## Internet.Use Smartphone Age Conservativeness
## Min. :0.0000 Min. :0.000 Min. :18.00 Min. :1.00
## 1st Qu.:1.0000 1st Qu.:0.000 1st Qu.:36.25 1st Qu.:3.00
## Median :1.0000 Median :0.000 Median :55.00 Median :3.00
## Mean :0.7745 Mean :0.499 Mean :52.25 Mean :3.28
## 3rd Qu.:1.0000 3rd Qu.:1.000 3rd Qu.:66.00 3rd Qu.:4.00
## Max. :1.0000 Max. :1.000 Max. :96.00 Max. :5.00
## Info.On.Internet Worry.About.Info Anonymity.Possible
## Min. : 0.000 Min. :0.0000 Min. :0.0000
## 1st Qu.: 0.000 1st Qu.:0.0000 1st Qu.:0.0000
## Median : 3.000 Median :0.0000 Median :0.0000
## Mean : 3.009 Mean :0.3862 Mean :0.3473
## 3rd Qu.: 5.000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :11.000 Max. :1.0000 Max. :1.0000
## Tried.Masking.Identity Privacy.Laws.Effective Sex.fctr
## Min. :0.0000 Min. :0.0000 Female:505
## 1st Qu.:0.0000 1st Qu.:0.0000 Male :497
## Median :0.0000 Median :0.0000
## Mean :0.1287 Mean :0.2685
## 3rd Qu.:0.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000
## Region.fctr
## South :359
## Midwest :239
## Northeast:166
## West :238
##
## ## [1] "numeric data missing in glbObsAll: "
## Internet.Use Smartphone Age
## 1 43 27
## Conservativeness Info.On.Internet Worry.About.Info
## 62 210 212
## Privacy.Importance Anonymity.Possible Tried.Masking.Identity
## 215 249 218
## Privacy.Laws.Effective
## 108
## [1] "numeric data w/ 0s in glbObsAll: "
## Internet.Use Smartphone
## 226 472
## Info.On.Internet Worry.About.Info
## 105 404
## Privacy.Importance Anonymity.Possible
## 43 475
## Tried.Masking.Identity Privacy.Laws.Effective
## 656 660
## Internet.Use.nonNA Smartphone.nonNA
## 226 502
## Info.On.Internet.nonNA Worry.About.Info.nonNA
## 314 615
## Anonymity.Possible.nonNA Tried.Masking.Identity.nonNA
## 654 873
## Privacy.Laws.Effective.nonNA
## 733
## [1] "numeric data w/ Infs in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ NaNs in glbObsAll: "
## named integer(0)
## [1] "string data missing in glbObsAll: "
## Sex State Region
## 0 0 0## label step_major step_minor label_minor bgn end
## 12 manage.missing.data 4 0 0 53.071 58.025
## 13 cluster.data 5 0 0 58.025 NA
## elapsed
## 12 4.954
## 13 NAStep 5.0: cluster data
## label step_major step_minor label_minor bgn end
## 13 cluster.data 5 0 0 58.025 58.08
## 14 partition.data.training 6 0 0 58.081 NA
## elapsed
## 13 0.056
## 14 NAStep 6.0: partition data training
## [1] "partition.data.training chunk: setup: elapsed: 0.00 secs"## Loading required package: caTools## [1] "partition.data.training chunk: Fit/OOB partition complete: elapsed: 0.12 secs"## .category .n.Fit .n.OOB .n.Tst .freqRatio.Fit .freqRatio.OOB
## 1 .dummy 541 246 215 1 1
## .freqRatio.Tst
## 1 1## [1] "glbObsAll: "## [1] 1002 31## [1] "glbObsTrn: "## [1] 787 31## [1] "glbObsFit: "## [1] 541 30## [1] "glbObsOOB: "## [1] 246 30## [1] "glbObsNew: "## [1] 215 30## [1] "partition.data.training chunk: teardown: elapsed: 0.27 secs"## label step_major step_minor label_minor bgn end
## 14 partition.data.training 6 0 0 58.081 58.412
## 15 select.features 7 0 0 58.413 NA
## elapsed
## 14 0.331
## 15 NAStep 7.0: select features
## Warning in cor(data.matrix(entity_df[, sel_feats]), y =
## as.numeric(entity_df[, : the standard deviation is zero## Loading required package: reshape2## [1] "cor(.pos, .pos.y)=1.0000"
## [1] "cor(Privacy.Importance, .pos)=-0.0026"
## [1] "cor(Privacy.Importance, .pos.y)=-0.0026"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified .pos.y as highly correlated with .pos## [1] "cor(.pos, .rownames)=0.9988"
## [1] "cor(Privacy.Importance, .pos)=-0.0026"
## [1] "cor(Privacy.Importance, .rownames)=-0.0031"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified .pos as highly correlated with .rownames## cor.y exclude.as.feat cor.y.abs
## Worry.About.Info 0.3125616494 1 0.3125616494
## Worry.About.Info.nonNA 0.3123675523 0 0.3123675523
## Tried.Masking.Identity 0.0957845093 1 0.0957845093
## Tried.Masking.Identity.nonNA 0.0952267205 0 0.0952267205
## Internet.Use 0.0855966388 1 0.0855966388
## Internet.Use.nonNA 0.0855966388 0 0.0855966388
## Smartphone 0.0331533206 1 0.0331533206
## Smartphone.nonNA 0.0317756005 0 0.0317756005
## Conservativeness.nonNA 0.0316345711 0 0.0316345711
## Age 0.0263455103 1 0.0263455103
## Conservativeness 0.0225366859 1 0.0225366859
## Age.nonNA 0.0177580510 0 0.0177580510
## Info.On.Internet 0.0139387475 1 0.0139387475
## Info.On.Internet.nonNA 0.0139387475 0 0.0139387475
## Region.fctr 0.0020435418 0 0.0020435418
## .rnorm 0.0006696934 0 0.0006696934
## .pos -0.0026437693 0 0.0026437693
## .pos.y -0.0026437693 0 0.0026437693
## .rownames -0.0031010249 0 0.0031010249
## Sex.fctr -0.0627285533 0 0.0627285533
## Anonymity.Possible -0.0950050501 1 0.0950050501
## Anonymity.Possible.nonNA -0.0952690406 0 0.0952690406
## Privacy.Laws.Effective.nonNA -0.1970234789 0 0.1970234789
## Privacy.Laws.Effective -0.2111497655 1 0.2111497655
## .category NA 1 NA
## cor.high.X freqRatio percentUnique zeroVar
## Worry.About.Info <NA> 1.033679 0.2541296 FALSE
## Worry.About.Info.nonNA <NA> 1.033592 0.2541296 FALSE
## Tried.Masking.Identity <NA> 5.085938 0.2541296 FALSE
## Tried.Masking.Identity.nonNA <NA> 5.100775 0.2541296 FALSE
## Internet.Use <NA> 51.466667 0.2541296 FALSE
## Internet.Use.nonNA <NA> 51.466667 0.2541296 FALSE
## Smartphone <NA> 1.710247 0.2541296 FALSE
## Smartphone.nonNA <NA> 1.713793 0.2541296 FALSE
## Conservativeness.nonNA <NA> 1.154762 0.6353240 FALSE
## Age <NA> 1.166667 9.0216010 FALSE
## Conservativeness <NA> 1.154812 0.6353240 FALSE
## Age.nonNA <NA> 1.208333 9.0216010 FALSE
## Info.On.Internet <NA> 1.019608 1.5247776 FALSE
## Info.On.Internet.nonNA <NA> 1.019608 1.5247776 FALSE
## Region.fctr <NA> 1.408867 0.5082592 FALSE
## .rnorm <NA> 1.000000 100.0000000 FALSE
## .pos .rownames 1.000000 100.0000000 FALSE
## .pos.y .pos 1.000000 100.0000000 FALSE
## .rownames <NA> 1.000000 100.0000000 FALSE
## Sex.fctr <NA> 1.017949 0.2541296 FALSE
## Anonymity.Possible <NA> 1.710145 0.2541296 FALSE
## Anonymity.Possible.nonNA <NA> 1.704467 0.2541296 FALSE
## Privacy.Laws.Effective.nonNA <NA> 2.820388 0.2541296 FALSE
## Privacy.Laws.Effective <NA> 2.908108 0.2541296 FALSE
## .category <NA> 0.000000 0.1270648 TRUE
## nzv is.cor.y.abs.low
## Worry.About.Info FALSE FALSE
## Worry.About.Info.nonNA FALSE FALSE
## Tried.Masking.Identity FALSE FALSE
## Tried.Masking.Identity.nonNA FALSE FALSE
## Internet.Use TRUE FALSE
## Internet.Use.nonNA TRUE FALSE
## Smartphone FALSE FALSE
## Smartphone.nonNA FALSE FALSE
## Conservativeness.nonNA FALSE FALSE
## Age FALSE FALSE
## Conservativeness FALSE FALSE
## Age.nonNA FALSE FALSE
## Info.On.Internet FALSE FALSE
## Info.On.Internet.nonNA FALSE FALSE
## Region.fctr FALSE FALSE
## .rnorm FALSE FALSE
## .pos FALSE FALSE
## .pos.y FALSE FALSE
## .rownames FALSE FALSE
## Sex.fctr FALSE FALSE
## Anonymity.Possible FALSE FALSE
## Anonymity.Possible.nonNA FALSE FALSE
## Privacy.Laws.Effective.nonNA FALSE FALSE
## Privacy.Laws.Effective FALSE FALSE
## .category TRUE NA## Warning in myplot_scatter(plt_feats_df, "percentUnique", "freqRatio",
## colorcol_name = "nzv", : converting nzv to class:factor## Warning: Removed 4 rows containing missing values (geom_point).
## Warning: Removed 4 rows containing missing values (geom_point).
## Warning: Removed 4 rows containing missing values (geom_point).
## cor.y exclude.as.feat cor.y.abs cor.high.X
## Internet.Use 0.08559664 1 0.08559664 <NA>
## Internet.Use.nonNA 0.08559664 0 0.08559664 <NA>
## .category NA 1 NA <NA>
## freqRatio percentUnique zeroVar nzv is.cor.y.abs.low
## Internet.Use 51.46667 0.2541296 FALSE TRUE FALSE
## Internet.Use.nonNA 51.46667 0.2541296 FALSE TRUE FALSE
## .category 0.00000 0.1270648 TRUE TRUE NA
## [1] "numeric data missing in glbObsAll: "
## Internet.Use Smartphone Age
## 1 43 27
## Conservativeness Info.On.Internet Worry.About.Info
## 62 210 212
## Privacy.Importance Anonymity.Possible Tried.Masking.Identity
## 215 249 218
## Privacy.Laws.Effective
## 108
## [1] "numeric data w/ 0s in glbObsAll: "
## Internet.Use Smartphone
## 226 472
## Info.On.Internet Worry.About.Info
## 105 404
## Privacy.Importance Anonymity.Possible
## 43 475
## Tried.Masking.Identity Privacy.Laws.Effective
## 656 660
## Internet.Use.nonNA Smartphone.nonNA
## 226 502
## Info.On.Internet.nonNA Worry.About.Info.nonNA
## 314 615
## Anonymity.Possible.nonNA Tried.Masking.Identity.nonNA
## 654 873
## Privacy.Laws.Effective.nonNA
## 733
## [1] "numeric data w/ Infs in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ NaNs in glbObsAll: "
## named integer(0)
## [1] "string data missing in glbObsAll: "
## Sex State Region .lcn
## 0 0 0 215## [1] "glb_feats_df:"## [1] 25 12## id exclude.as.feat rsp_var
## Privacy.Importance Privacy.Importance TRUE TRUE## id cor.y exclude.as.feat cor.y.abs
## Privacy.Importance Privacy.Importance NA TRUE NA
## cor.high.X freqRatio percentUnique zeroVar nzv
## Privacy.Importance <NA> NA NA NA NA
## is.cor.y.abs.low interaction.feat shapiro.test.p.value
## Privacy.Importance NA NA NA
## rsp_var_raw rsp_var
## Privacy.Importance NA TRUE## [1] "glb_feats_df vs. glbObsAll: "## character(0)## [1] "glbObsAll vs. glb_feats_df: "## character(0)## label step_major step_minor label_minor bgn end elapsed
## 15 select.features 7 0 0 58.413 60.261 1.848
## 16 fit.models 8 0 0 60.262 NA NAStep 8.0: fit models
fit.models_0_chunk_df <- myadd_chunk(NULL, "fit.models_0_bgn", label.minor = "setup")## label step_major step_minor label_minor bgn end elapsed
## 1 fit.models_0_bgn 1 0 setup 60.827 NA NA# load(paste0(glbOut$pfx, "dsk.RData"))
get_model_sel_frmla <- function() {
model_evl_terms <- c(NULL)
# min.aic.fit might not be avl
lclMdlEvlCriteria <-
glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)]
for (metric in lclMdlEvlCriteria)
model_evl_terms <- c(model_evl_terms,
ifelse(length(grep("max", metric)) > 0, "-", "+"), metric)
if (glb_is_classification && glb_is_binomial)
model_evl_terms <- c(model_evl_terms, "-", "opt.prob.threshold.OOB")
model_sel_frmla <- as.formula(paste(c("~ ", model_evl_terms), collapse = " "))
return(model_sel_frmla)
}
get_dsp_models_df <- function() {
dsp_models_cols <- c("id",
glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE))
dsp_models_df <-
#orderBy(get_model_sel_frmla(), glb_models_df)[, c("id", glbMdlMetricsEval)]
orderBy(get_model_sel_frmla(), glb_models_df)[, dsp_models_cols]
nCvMdl <- sapply(glb_models_lst, function(mdl) nrow(mdl$results))
nParams <- sapply(glb_models_lst, function(mdl) ifelse(mdl$method == "custom", 0,
nrow(subset(modelLookup(mdl$method), parameter != "parameter"))))
# nCvMdl <- nCvMdl[names(nCvMdl) != "avNNet"]
# nParams <- nParams[names(nParams) != "avNNet"]
if (length(cvMdlProblems <- nCvMdl[nCvMdl <= nParams]) > 0) {
print("Cross Validation issues:")
warning("Cross Validation issues:")
print(cvMdlProblems)
}
pltMdls <- setdiff(names(nCvMdl), names(cvMdlProblems))
pltMdls <- setdiff(pltMdls, names(nParams[nParams == 0]))
# length(pltMdls) == 21
png(paste0(glbOut$pfx, "bestTune.png"), width = 480 * 2, height = 480 * 4)
grid.newpage()
pushViewport(viewport(layout = grid.layout(ceiling(length(pltMdls) / 2.0), 2)))
pltIx <- 1
for (mdlId in pltMdls) {
print(ggplot(glb_models_lst[[mdlId]], highBestTune = TRUE) + labs(title = mdlId),
vp = viewport(layout.pos.row = ceiling(pltIx / 2.0),
layout.pos.col = ((pltIx - 1) %% 2) + 1))
pltIx <- pltIx + 1
}
dev.off()
if (all(row.names(dsp_models_df) != dsp_models_df$id))
row.names(dsp_models_df) <- dsp_models_df$id
return(dsp_models_df)
}
#get_dsp_models_df()
if (glb_is_classification && glb_is_binomial &&
(length(unique(glbObsFit[, glb_rsp_var])) < 2))
stop("glbObsFit$", glb_rsp_var, ": contains less than 2 unique values: ",
paste0(unique(glbObsFit[, glb_rsp_var]), collapse=", "))
max_cor_y_x_vars <- orderBy(~ -cor.y.abs,
subset(glb_feats_df, (exclude.as.feat == 0) & !nzv & !is.cor.y.abs.low &
is.na(cor.high.X)))[1:2, "id"]
max_cor_y_x_vars <- max_cor_y_x_vars[!is.na(max_cor_y_x_vars)]
if (length(max_cor_y_x_vars) < 2)
max_cor_y_x_vars <- union(max_cor_y_x_vars, ".pos")
if (!is.null(glb_Baseline_mdl_var)) {
if ((max_cor_y_x_vars[1] != glb_Baseline_mdl_var) &
(glb_feats_df[glb_feats_df$id == max_cor_y_x_vars[1], "cor.y.abs"] >
glb_feats_df[glb_feats_df$id == glb_Baseline_mdl_var, "cor.y.abs"]))
stop(max_cor_y_x_vars[1], " has a higher correlation with ", glb_rsp_var,
" than the Baseline var: ", glb_Baseline_mdl_var)
}
glb_model_type <- ifelse(glb_is_regression, "regression", "classification")
# Model specs
# c("id.prefix", "method", "type",
# # trainControl params
# "preProc.method", "cv.n.folds", "cv.n.repeats", "summary.fn",
# # train params
# "metric", "metric.maximize", "tune.df")
# Baseline
if (!is.null(glb_Baseline_mdl_var)) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Baseline"), major.inc = FALSE,
label.minor = "mybaseln_classfr")
ret_lst <- myfit_mdl(mdl_id="Baseline",
model_method="mybaseln_classfr",
indepVar=glb_Baseline_mdl_var,
rsp_var=glb_rsp_var,
fit_df=glbObsFit, OOB_df=glbObsOOB)
}
# Most Frequent Outcome "MFO" model: mean(y) for regression
# Not using caret's nullModel since model stats not avl
# Cannot use rpart for multinomial classification since it predicts non-MFO
if (glb_is_classification) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "MFO"), major.inc = FALSE,
label.minor = "myMFO_classfr")
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "MFO", type = glb_model_type, trainControl.method = "none",
train.method = ifelse(glb_is_regression, "lm", "myMFO_classfr"))),
indepVar = ".rnorm", rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
# "random" model - only for classification;
# none needed for regression since it is same as MFO
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Random"), major.inc = FALSE,
label.minor = "myrandom_classfr")
#stop(here"); glb2Sav(); all.equal(glb_models_df, sav_models_df)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Random", type = glb_model_type, trainControl.method = "none",
train.method = "myrandom_classfr")),
indepVar = ".rnorm", rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
# Max.cor.Y
# Check impact of cv
# rpart is not a good candidate since caret does not optimize cp (only tuning parameter of rpart) well
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Max.cor.Y.rcv.*X*"), major.inc = FALSE,
label.minor = "glmnet")## label step_major step_minor label_minor bgn
## 1 fit.models_0_bgn 1 0 setup 60.827
## 2 fit.models_0_Max.cor.Y.rcv.*X* 1 1 glmnet 60.863
## end elapsed
## 1 60.862 0.035
## 2 NA NAret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.rcv.1X1", type = glb_model_type, trainControl.method = "none",
train.method = "glmnet")),
indepVar = max_cor_y_x_vars, rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: Max.cor.Y.rcv.1X1###glmnet"
## [1] " indepVar: Worry.About.Info.nonNA,Privacy.Laws.Effective.nonNA"
## [1] "myfit_mdl: setup complete: 0.749000 secs"## Loading required package: glmnet## Loading required package: Matrix## Loaded glmnet 2.0-5## Fitting alpha = 0.1, lambda = 0.192 on full training set
## [1] "myfit_mdl: train complete: 1.760000 secs"
## Length Class Mode
## a0 69 -none- numeric
## beta 138 dgCMatrix S4
## df 69 -none- numeric
## dim 2 -none- numeric
## lambda 69 -none- numeric
## dev.ratio 69 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 2 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 1 -none- logical
## [1] "min lambda > lambdaOpt:"
## (Intercept) Privacy.Laws.Effective.nonNA
## 56.146689 -7.954041
## Worry.About.Info.nonNA
## 18.096976
## [1] "max lambda < lambdaOpt:"
## (Intercept) Privacy.Laws.Effective.nonNA
## 56.14217 -7.96052
## Worry.About.Info.nonNA
## 18.10932
## [1] "myfit_mdl: train diagnostics complete: 1.845000 secs"
## [1] "myfit_mdl: predict complete: 2.001000 secs"
## id
## 1 Max.cor.Y.rcv.1X1###glmnet
## feats max.nTuningRuns
## 1 Worry.About.Info.nonNA,Privacy.Laws.Effective.nonNA 0
## min.elapsedtime.everything min.elapsedtime.final max.R.sq.fit
## 1 1.005 0.01 0.1049928
## min.RMSE.fit max.Adj.R.sq.fit max.R.sq.OOB min.RMSE.OOB max.Adj.R.sq.OOB
## 1 29.76034 0.1016656 0.1490914 29.00863 0.1420881
## [1] "myfit_mdl: exit: 2.007000 secs"if (glbMdlCheckRcv) {
# rcv_n_folds == 1 & rcv_n_repeats > 1 crashes
for (rcv_n_folds in seq(3, glb_rcv_n_folds + 2, 2))
for (rcv_n_repeats in seq(1, glb_rcv_n_repeats + 2, 2)) {
# Experiment specific code to avoid caret crash
# lcl_tune_models_df <- rbind(data.frame()
# ,data.frame(method = "glmnet", parameter = "alpha",
# vals = "0.100 0.325 0.550 0.775 1.000")
# ,data.frame(method = "glmnet", parameter = "lambda",
# vals = "9.342e-02")
# )
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
list(
id.prefix = paste0("Max.cor.Y.rcv.", rcv_n_folds, "X", rcv_n_repeats),
type = glb_model_type,
# tune.df = lcl_tune_models_df,
trainControl.method = "repeatedcv",
trainControl.number = rcv_n_folds,
trainControl.repeats = rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.method = "glmnet", train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize)),
indepVar = max_cor_y_x_vars, rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
# Add parallel coordinates graph of glb_models_df[, glbMdlMetricsEval] to evaluate cv parameters
tmp_models_cols <- c("id", "max.nTuningRuns",
glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE))
print(myplot_parcoord(obs_df = subset(glb_models_df,
grepl("Max.cor.Y.rcv.", id, fixed = TRUE),
select = -feats)[, tmp_models_cols],
id_var = "id"))
}
# Useful for stacking decisions
# fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
# paste0("fit.models_0_", "Max.cor.Y[rcv.1X1.cp.0|]"), major.inc = FALSE,
# label.minor = "rpart")
#
# ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
# id.prefix = "Max.cor.Y.rcv.1X1.cp.0", type = glb_model_type, trainControl.method = "none",
# train.method = "rpart",
# tune.df=data.frame(method="rpart", parameter="cp", min=0.0, max=0.0, by=0.1))),
# indepVar=max_cor_y_x_vars, rsp_var=glb_rsp_var,
# fit_df=glbObsFit, OOB_df=glbObsOOB)
#stop(here"); glb2Sav(); all.equal(glb_models_df, sav_models_df)
# if (glb_is_regression || glb_is_binomial) # For multinomials this model will be run next by default
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y",
type = glb_model_type, trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds,
trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "rpart")),
indepVar = max_cor_y_x_vars, rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: Max.cor.Y##rcv#rpart"
## [1] " indepVar: Worry.About.Info.nonNA,Privacy.Laws.Effective.nonNA"
## [1] "myfit_mdl: setup complete: 1.075000 secs"## Loading required package: rpart## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info =
## trainInfo, : There were missing values in resampled performance measures.## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0 on full training set
## [1] "myfit_mdl: train complete: 2.546000 secs"## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = "Max.cor.Y", : model's bestTune found at an extreme of
## tuneGrid for parameter: cp## Loading required package: rpart.plot
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 541
##
## CP nsplit rel error
## 1 0.092784080 0 1.0000000
## 2 0.011825075 1 0.9072159
## 3 0.002723562 2 0.8953908
## 4 0.000000000 3 0.8926673
##
## Variable importance
## Worry.About.Info.nonNA Privacy.Laws.Effective.nonNA
## 79 21
##
## Node number 1: 541 observations, complexity param=0.09278408
## mean=63.16826, MSE=989.5762
## left son=2 (270 obs) right son=3 (271 obs)
## Primary splits:
## Worry.About.Info.nonNA < 0.5 to the left, improve=0.09278408, (0 missing)
## Privacy.Laws.Effective.nonNA < 0.5 to the right, improve=0.02244773, (0 missing)
## Surrogate splits:
## Privacy.Laws.Effective.nonNA < 0.5 to the right, agree=0.558, adj=0.115, (0 split)
##
## Node number 2: 270 observations, complexity param=0.002723562
## mean=53.56842, MSE=1066.706
## left son=4 (85 obs) right son=5 (185 obs)
## Primary splits:
## Privacy.Laws.Effective.nonNA < 0.5 to the right, improve=0.005062616, (0 missing)
##
## Node number 3: 271 observations, complexity param=0.01182508
## mean=72.73268, MSE=729.4356
## left son=6 (54 obs) right son=7 (217 obs)
## Primary splits:
## Privacy.Laws.Effective.nonNA < 0.5 to the right, improve=0.03202537, (0 missing)
##
## Node number 4: 85 observations
## mean=50.14006, MSE=1085.702
##
## Node number 5: 185 observations
## mean=55.14361, MSE=1050.097
##
## Node number 6: 54 observations
## mean=63.0438, MSE=855.2883
##
## Node number 7: 217 observations
## mean=75.14373, MSE=668.9437
##
## n= 541
##
## node), split, n, deviance, yval
## * denotes terminal node
##
## 1) root 541 535360.70 63.16826
## 2) Worry.About.Info.nonNA< 0.5 270 288010.70 53.56842
## 4) Privacy.Laws.Effective.nonNA>=0.5 85 92284.70 50.14006 *
## 5) Privacy.Laws.Effective.nonNA< 0.5 185 194267.90 55.14361 *
## 3) Worry.About.Info.nonNA>=0.5 271 197677.00 72.73268
## 6) Privacy.Laws.Effective.nonNA>=0.5 54 46185.57 63.04380 *
## 7) Privacy.Laws.Effective.nonNA< 0.5 217 145160.80 75.14373 *
## [1] "myfit_mdl: train diagnostics complete: 3.520000 secs"
## [1] "myfit_mdl: predict complete: 3.545000 secs"
## id feats
## 1 Max.cor.Y##rcv#rpart Worry.About.Info.nonNA,Privacy.Laws.Effective.nonNA
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 5 1.467 0.007
## max.R.sq.fit min.RMSE.fit max.Adj.R.sq.fit max.R.sq.OOB min.RMSE.OOB
## 1 0.1073327 29.91502 NA 0.1353391 29.24211
## max.Adj.R.sq.OOB max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 NA 0.1034143 0.812932 0.04284193
## [1] "myfit_mdl: exit: 3.556000 secs"if ((length(glbFeatsDateTime) > 0) &&
(sum(grepl(paste(names(glbFeatsDateTime), "\\.day\\.minutes\\.poly\\.", sep = ""),
names(glbObsAll))) > 0)) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Max.cor.Y.Time.Poly"), major.inc = FALSE,
label.minor = "glmnet")
indepVars <- c(max_cor_y_x_vars,
grep(paste(names(glbFeatsDateTime), "\\.day\\.minutes\\.poly\\.", sep = ""),
names(glbObsAll), value = TRUE))
indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Time.Poly",
type = glb_model_type, trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
if ((length(glbFeatsDateTime) > 0) &&
(sum(grepl(paste(names(glbFeatsDateTime), "\\.last[[:digit:]]", sep = ""),
names(glbObsAll))) > 0)) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Max.cor.Y.Time.Lag"), major.inc = FALSE,
label.minor = "glmnet")
indepVars <- c(max_cor_y_x_vars,
grep(paste(names(glbFeatsDateTime), "\\.last[[:digit:]]", sep = ""),
names(glbObsAll), value = TRUE))
indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Time.Lag",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
if (length(glbFeatsText) > 0) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Txt.*"), major.inc = FALSE,
label.minor = "glmnet")
indepVars <- c(max_cor_y_x_vars)
for (txtFeat in names(glbFeatsText))
indepVars <- union(indepVars,
grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.(?!([T|P]\\.))", sep = ""),
names(glbObsAll), perl = TRUE, value = TRUE))
indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Text.nonTP",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
indepVars <- c(max_cor_y_x_vars)
for (txtFeat in names(glbFeatsText))
indepVars <- union(indepVars,
grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.T\\.", sep = ""),
names(glbObsAll), perl = TRUE, value = TRUE))
indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Text.onlyT",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
indepVars <- c(max_cor_y_x_vars)
for (txtFeat in names(glbFeatsText))
indepVars <- union(indepVars,
grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.P\\.", sep = ""),
names(glbObsAll), perl = TRUE, value = TRUE))
indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Text.onlyP",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
# Interactions.High.cor.Y
if (length(int_feats <- setdiff(setdiff(unique(glb_feats_df$cor.high.X), NA),
subset(glb_feats_df, nzv)$id)) > 0) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Interact.High.cor.Y"), major.inc = FALSE,
label.minor = "glmnet")
ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
id.prefix="Interact.High.cor.Y",
type=glb_model_type, trainControl.method="repeatedcv",
trainControl.number=glb_rcv_n_folds, trainControl.repeats=glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method="glmnet")),
indepVar=c(max_cor_y_x_vars, paste(max_cor_y_x_vars[1], int_feats, sep=":")),
rsp_var=glb_rsp_var,
fit_df=glbObsFit, OOB_df=glbObsOOB)
} ## label step_major step_minor label_minor
## 2 fit.models_0_Max.cor.Y.rcv.*X* 1 1 glmnet
## 3 fit.models_0_Interact.High.cor.Y 1 2 glmnet
## bgn end elapsed
## 2 60.863 66.597 5.734
## 3 66.597 NA NA
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: Interact.High.cor.Y##rcv#glmnet"
## [1] " indepVar: Worry.About.Info.nonNA,Privacy.Laws.Effective.nonNA,Worry.About.Info.nonNA:.rownames,Worry.About.Info.nonNA:.pos"
## [1] "myfit_mdl: setup complete: 1.041000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.1, lambda = 0.89 on full training set
## [1] "myfit_mdl: train complete: 2.733000 secs"## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = "Interact.High.cor.Y", : model's bestTune found at an
## extreme of tuneGrid for parameter: alpha
## Length Class Mode
## a0 73 -none- numeric
## beta 292 dgCMatrix S4
## df 73 -none- numeric
## dim 2 -none- numeric
## lambda 73 -none- numeric
## dev.ratio 73 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 4 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 1 -none- logical
## [1] "min lambda > lambdaOpt:"
## (Intercept) Privacy.Laws.Effective.nonNA
## 56.316007359 -7.803073334
## Worry.About.Info.nonNA Worry.About.Info.nonNA:.pos
## 14.899877792 0.003177974
## Worry.About.Info.nonNA:.rownames
## 0.002624699
## [1] "max lambda < lambdaOpt:"
## (Intercept) Privacy.Laws.Effective.nonNA
## 56.300025186 -7.830634795
## Worry.About.Info.nonNA Worry.About.Info.nonNA:.pos
## 14.986932230 0.003172958
## Worry.About.Info.nonNA:.rownames
## 0.002554784
## [1] "myfit_mdl: train diagnostics complete: 3.462000 secs"
## [1] "myfit_mdl: predict complete: 3.600000 secs"
## id
## 1 Interact.High.cor.Y##rcv#glmnet
## feats
## 1 Worry.About.Info.nonNA,Privacy.Laws.Effective.nonNA,Worry.About.Info.nonNA:.rownames,Worry.About.Info.nonNA:.pos
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 1.688 0.006
## max.R.sq.fit min.RMSE.fit max.Adj.R.sq.fit max.R.sq.OOB min.RMSE.OOB
## 1 0.1056166 29.9875 0.09894209 0.1437998 29.09869
## max.Adj.R.sq.OOB max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.129589 0.1013255 0.8194642 0.04849231
## [1] "myfit_mdl: exit: 3.610000 secs"# Low.cor.X
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Low.cor.X"), major.inc = FALSE,
label.minor = "glmnet")## label step_major step_minor label_minor
## 3 fit.models_0_Interact.High.cor.Y 1 2 glmnet
## 4 fit.models_0_Low.cor.X 1 3 glmnet
## bgn end elapsed
## 3 66.597 70.219 3.622
## 4 70.219 NA NAindepVar <- mygetIndepVar(glb_feats_df)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Low.cor.X",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVar, rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: Low.cor.X##rcv#glmnet"
## [1] " indepVar: Worry.About.Info.nonNA,Tried.Masking.Identity.nonNA,Smartphone.nonNA,Conservativeness.nonNA,Age.nonNA,Info.On.Internet.nonNA,Region.fctr,.rnorm,.pos,.pos.y,.rownames,Sex.fctr,Anonymity.Possible.nonNA,Privacy.Laws.Effective.nonNA"
## [1] "myfit_mdl: setup complete: 0.716000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.325, lambda = 4.13 on full training set
## [1] "myfit_mdl: train complete: 2.450000 secs"## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = "Low.cor.X", : model's bestTune found at an extreme of
## tuneGrid for parameter: lambda
## Length Class Mode
## a0 69 -none- numeric
## beta 1104 dgCMatrix S4
## df 69 -none- numeric
## dim 2 -none- numeric
## lambda 69 -none- numeric
## dev.ratio 69 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 16 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 1 -none- logical
## [1] "min lambda > lambdaOpt:"
## (Intercept) Anonymity.Possible.nonNA
## 58.0050850 -1.5358027
## Privacy.Laws.Effective.nonNA Sex.fctrMale
## -5.0124203 -1.8472409
## Smartphone.nonNA Tried.Masking.Identity.nonNA
## 0.6342123 2.3713975
## Worry.About.Info.nonNA
## 14.2106322
## [1] "max lambda < lambdaOpt:"
## (Intercept) .rownames
## 57.9734929002 -0.0001035325
## Anonymity.Possible.nonNA Privacy.Laws.Effective.nonNA
## -1.7465342054 -5.2684828482
## Sex.fctrMale Smartphone.nonNA
## -2.0623865283 0.8898792979
## Tried.Masking.Identity.nonNA Worry.About.Info.nonNA
## 2.6040929372 14.4798655627
## [1] "myfit_mdl: train diagnostics complete: 3.052000 secs"
## [1] "myfit_mdl: predict complete: 3.197000 secs"
## id
## 1 Low.cor.X##rcv#glmnet
## feats
## 1 Worry.About.Info.nonNA,Tried.Masking.Identity.nonNA,Smartphone.nonNA,Conservativeness.nonNA,Age.nonNA,Info.On.Internet.nonNA,Region.fctr,.rnorm,.pos,.pos.y,.rownames,Sex.fctr,Anonymity.Possible.nonNA,Privacy.Laws.Effective.nonNA
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 1.725 0.006
## max.R.sq.fit min.RMSE.fit max.Adj.R.sq.fit max.R.sq.OOB min.RMSE.OOB
## 1 0.1093654 30.04518 0.08217045 0.1346295 29.2541
## max.Adj.R.sq.OOB max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.07416698 0.0961782 0.6984881 0.03771691
## [1] "myfit_mdl: exit: 3.207000 secs"fit.models_0_chunk_df <-
myadd_chunk(fit.models_0_chunk_df, "fit.models_0_end", major.inc = FALSE,
label.minor = "teardown")## label step_major step_minor label_minor bgn end
## 4 fit.models_0_Low.cor.X 1 3 glmnet 70.219 73.449
## 5 fit.models_0_end 1 4 teardown 73.450 NA
## elapsed
## 4 3.231
## 5 NArm(ret_lst)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc = FALSE)## label step_major step_minor label_minor bgn end elapsed
## 16 fit.models 8 0 0 60.262 73.461 13.199
## 17 fit.models 8 1 1 73.461 NA NAfit.models_1_chunk_df <- myadd_chunk(NULL, "fit.models_1_bgn", label.minor = "setup")## label step_major step_minor label_minor bgn end elapsed
## 1 fit.models_1_bgn 1 0 setup 74.661 NA NA# refactor code for outliers / ensure all model runs exclude outliers in this chunk ???
#stop(here"); glb2Sav(); all.equal(glb_models_df, sav_models_df)
topindep_var <- NULL; interact_vars <- NULL;
for (mdl_id_pfx in names(glbMdlFamilies)) {
fit.models_1_chunk_df <-
myadd_chunk(fit.models_1_chunk_df, paste0("fit.models_1_", mdl_id_pfx),
major.inc = FALSE, label.minor = "setup")
indepVar <- NULL;
if (grepl("\\.Interact", mdl_id_pfx)) {
if (is.null(topindep_var) && is.null(interact_vars)) {
# select best glmnet model upto now
dsp_models_df <- orderBy(model_sel_frmla <- get_model_sel_frmla(),
glb_models_df)
dsp_models_df <- subset(dsp_models_df,
grepl(".glmnet", id, fixed = TRUE))
bst_mdl_id <- dsp_models_df$id[1]
mdl_id_pfx <-
paste(c(head(unlist(strsplit(bst_mdl_id, "[.]")), -1), "Interact"),
collapse=".")
# select important features
if (is.null(bst_featsimp_df <-
myget_feats_importance(glb_models_lst[[bst_mdl_id]]))) {
warning("Base model for RFE.Interact: ", bst_mdl_id,
" has no important features")
next
}
topindep_ix <- 1
while (is.null(topindep_var) && (topindep_ix <= nrow(bst_featsimp_df))) {
topindep_var <- row.names(bst_featsimp_df)[topindep_ix]
if (grepl(".fctr", topindep_var, fixed=TRUE))
topindep_var <-
paste0(unlist(strsplit(topindep_var, ".fctr"))[1], ".fctr")
if (topindep_var %in% names(glbFeatsInteractionOnly)) {
topindep_var <- NULL; topindep_ix <- topindep_ix + 1
} else break
}
# select features with importance > max(10, importance of .rnorm) & is not highest
# combine factor dummy features to just the factor feature
if (length(pos_rnorm <-
grep(".rnorm", row.names(bst_featsimp_df), fixed=TRUE)) > 0)
imp_rnorm <- bst_featsimp_df[pos_rnorm, 1] else
imp_rnorm <- NA
imp_cutoff <- max(10, imp_rnorm, na.rm=TRUE)
interact_vars <-
tail(row.names(subset(bst_featsimp_df,
imp > imp_cutoff)), -1)
if (length(interact_vars) > 0) {
interact_vars <-
myadjustInteractionFeats(glb_feats_df, myextract_actual_feats(interact_vars))
interact_vars <-
interact_vars[!grepl(topindep_var, interact_vars, fixed=TRUE)]
}
### bid0_sp only
# interact_vars <- c(
# "biddable", "D.ratio.sum.TfIdf.wrds.n", "D.TfIdf.sum.stem.stop.Ratio", "D.sum.TfIdf",
# "D.TfIdf.sum.post.stop", "D.TfIdf.sum.post.stem", "D.ratio.wrds.stop.n.wrds.n", "D.chrs.uppr.n.log",
# "D.chrs.n.log", "color.fctr"
# # , "condition.fctr", "prdl.my.descr.fctr"
# )
# interact_vars <- setdiff(interact_vars, c("startprice.dgt2.is9", "color.fctr"))
###
indepVar <- myextract_actual_feats(row.names(bst_featsimp_df))
indepVar <- setdiff(indepVar, topindep_var)
if (length(interact_vars) > 0) {
indepVar <-
setdiff(indepVar, myextract_actual_feats(interact_vars))
indepVar <- c(indepVar,
paste(topindep_var, setdiff(interact_vars, topindep_var),
sep = "*"))
} else indepVar <- union(indepVar, topindep_var)
}
}
if (is.null(indepVar))
indepVar <- glb_mdl_feats_lst[[mdl_id_pfx]]
if (is.null(indepVar) && grepl("RFE\\.", mdl_id_pfx))
indepVar <- myextract_actual_feats(predictors(rfe_fit_results))
if (is.null(indepVar))
indepVar <- mygetIndepVar(glb_feats_df)
if ((length(indepVar) == 1) && (grepl("^%<d-%", indepVar))) {
indepVar <-
eval(parse(text = str_trim(unlist(strsplit(indepVar, "%<d-%"))[2])))
}
indepVar <- myadjustInteractionFeats(glb_feats_df, indepVar)
if (grepl("\\.Interact", mdl_id_pfx)) {
# if (method != tail(unlist(strsplit(bst_mdl_id, "[.]")), 1)) next
if (is.null(glbMdlFamilies[[mdl_id_pfx]])) {
if (!is.null(glbMdlFamilies[["Best.Interact"]]))
glbMdlFamilies[[mdl_id_pfx]] <-
glbMdlFamilies[["Best.Interact"]]
}
}
if (!is.null(glbObsFitOutliers[[mdl_id_pfx]])) {
fitobs_df <- glbObsFit[!(glbObsFit[, glbFeatsId] %in%
glbObsFitOutliers[[mdl_id_pfx]]), ]
print(sprintf("Outliers removed: %d", nrow(glbObsFit) - nrow(fitobs_df)))
print(setdiff(glbObsFit[, glbFeatsId], fitobs_df[, glbFeatsId]))
} else fitobs_df <- glbObsFit
if (is.null(glbMdlFamilies[[mdl_id_pfx]]))
mdl_methods <- glbMdlMethods else
mdl_methods <- glbMdlFamilies[[mdl_id_pfx]]
for (method in mdl_methods) {
if (method %in% c("rpart", "rf")) {
# rpart: fubar's the tree
# rf: skip the scenario w/ .rnorm for speed
indepVar <- setdiff(indepVar, c(".rnorm"))
#mdl_id <- paste0(mdl_id_pfx, ".no.rnorm")
}
fit.models_1_chunk_df <- myadd_chunk(fit.models_1_chunk_df,
paste0("fit.models_1_", mdl_id_pfx), major.inc = FALSE,
label.minor = method)
ret_lst <-
myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = mdl_id_pfx,
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv", # or "none" if nominalWorkflow is crashing
trainControl.number = glb_rcv_n_folds,
trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = method)),
indepVar = indepVar, rsp_var = glb_rsp_var,
fit_df = fitobs_df, OOB_df = glbObsOOB)
# ntv_mdl <- glmnet(x = as.matrix(
# fitobs_df[, indepVar]),
# y = as.factor(as.character(
# fitobs_df[, glb_rsp_var])),
# family = "multinomial")
# bgn = 1; end = 100;
# ntv_mdl <- glmnet(x = as.matrix(
# subset(fitobs_df, pop.fctr != "crypto")[bgn:end, indepVar]),
# y = as.factor(as.character(
# subset(fitobs_df, pop.fctr != "crypto")[bgn:end, glb_rsp_var])),
# family = "multinomial")
}
}## label step_major step_minor label_minor bgn end
## 1 fit.models_1_bgn 1 0 setup 74.661 74.672
## 2 fit.models_1_All.X 1 1 setup 74.673 NA
## elapsed
## 1 0.011
## 2 NA
## label step_major step_minor label_minor bgn end
## 2 fit.models_1_All.X 1 1 setup 74.673 74.679
## 3 fit.models_1_All.X 1 2 glmnet 74.680 NA
## elapsed
## 2 0.007
## 3 NA
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: All.X##rcv#glmnet"
## [1] " indepVar: Worry.About.Info.nonNA,Tried.Masking.Identity.nonNA,Smartphone.nonNA,Conservativeness.nonNA,Age.nonNA,Info.On.Internet.nonNA,Region.fctr,.rnorm,.pos,.pos.y,.rownames,Sex.fctr,Anonymity.Possible.nonNA,Privacy.Laws.Effective.nonNA"
## [1] "myfit_mdl: setup complete: 0.746000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.325, lambda = 4.13 on full training set
## [1] "myfit_mdl: train complete: 2.710000 secs"## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: lambda
## Length Class Mode
## a0 69 -none- numeric
## beta 1104 dgCMatrix S4
## df 69 -none- numeric
## dim 2 -none- numeric
## lambda 69 -none- numeric
## dev.ratio 69 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 16 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 1 -none- logical
## [1] "min lambda > lambdaOpt:"
## (Intercept) Anonymity.Possible.nonNA
## 58.0050850 -1.5358027
## Privacy.Laws.Effective.nonNA Sex.fctrMale
## -5.0124203 -1.8472409
## Smartphone.nonNA Tried.Masking.Identity.nonNA
## 0.6342123 2.3713975
## Worry.About.Info.nonNA
## 14.2106322
## [1] "max lambda < lambdaOpt:"
## (Intercept) .rownames
## 57.9734929002 -0.0001035325
## Anonymity.Possible.nonNA Privacy.Laws.Effective.nonNA
## -1.7465342054 -5.2684828482
## Sex.fctrMale Smartphone.nonNA
## -2.0623865283 0.8898792979
## Tried.Masking.Identity.nonNA Worry.About.Info.nonNA
## 2.6040929372 14.4798655627
## [1] "myfit_mdl: train diagnostics complete: 3.392000 secs"
## [1] "myfit_mdl: predict complete: 3.537000 secs"
## id
## 1 All.X##rcv#glmnet
## feats
## 1 Worry.About.Info.nonNA,Tried.Masking.Identity.nonNA,Smartphone.nonNA,Conservativeness.nonNA,Age.nonNA,Info.On.Internet.nonNA,Region.fctr,.rnorm,.pos,.pos.y,.rownames,Sex.fctr,Anonymity.Possible.nonNA,Privacy.Laws.Effective.nonNA
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 1.956 0.006
## max.R.sq.fit min.RMSE.fit max.Adj.R.sq.fit max.R.sq.OOB min.RMSE.OOB
## 1 0.1093654 30.04518 0.08217045 0.1346295 29.2541
## max.Adj.R.sq.OOB max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.07416698 0.0961782 0.6984881 0.03771691
## [1] "myfit_mdl: exit: 3.546000 secs"
## label step_major step_minor label_minor bgn end
## 3 fit.models_1_All.X 1 2 glmnet 74.680 78.231
## 4 fit.models_1_All.X 1 3 glm 78.232 NA
## elapsed
## 3 3.552
## 4 NA
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: All.X##rcv#glm"
## [1] " indepVar: Worry.About.Info.nonNA,Tried.Masking.Identity.nonNA,Smartphone.nonNA,Conservativeness.nonNA,Age.nonNA,Info.On.Internet.nonNA,Region.fctr,.rnorm,.pos,.pos.y,.rownames,Sex.fctr,Anonymity.Possible.nonNA,Privacy.Laws.Effective.nonNA"
## [1] "myfit_mdl: setup complete: 0.721000 secs"
## Aggregating results
## Fitting final model on full training set
## [1] "myfit_mdl: train complete: 1.931000 secs"
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -77.027 -22.575 3.484 23.757 56.328
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 58.4640231 8.7994531 6.644 7.65e-11 ***
## .pos 0.0713709 0.1204938 0.592 0.55389
## .pos.y NA NA NA NA
## .rnorm -0.9576636 1.2513743 -0.765 0.44444
## .rownames -0.0626541 0.0963830 -0.650 0.51594
## Age.nonNA 0.0005857 0.0823976 0.007 0.99433
## Anonymity.Possible.nonNA -3.8500052 2.7259451 -1.412 0.15844
## Conservativeness.nonNA 1.0137857 1.3190808 0.769 0.44250
## Info.On.Internet.nonNA -0.2112855 0.5078327 -0.416 0.67754
## Privacy.Laws.Effective.nonNA -8.0409503 2.9796937 -2.699 0.00719 **
## Region.fctrMidwest 0.9832077 3.5991012 0.273 0.78482
## Region.fctrNortheast -0.2990229 3.8626493 -0.077 0.93832
## Region.fctrWest -0.0679459 3.3273974 -0.020 0.98372
## Sex.fctrMale -4.4066486 2.6438582 -1.667 0.09616 .
## Smartphone.nonNA 5.0442580 2.9295089 1.722 0.08568 .
## Tried.Masking.Identity.nonNA 5.2942970 3.5985298 1.471 0.14183
## Worry.About.Info.nonNA 17.6121420 2.6495456 6.647 7.50e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 889.9333)
##
## Null deviance: 535361 on 540 degrees of freedom
## Residual deviance: 467215 on 525 degrees of freedom
## AIC: 5227.1
##
## Number of Fisher Scoring iterations: 2
##
## [1] "myfit_mdl: train diagnostics complete: 2.697000 secs"## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## [1] "myfit_mdl: predict complete: 2.766000 secs"
## id
## 1 All.X##rcv#glm
## feats
## 1 Worry.About.Info.nonNA,Tried.Masking.Identity.nonNA,Smartphone.nonNA,Conservativeness.nonNA,Age.nonNA,Info.On.Internet.nonNA,Region.fctr,.rnorm,.pos,.pos.y,.rownames,Sex.fctr,Anonymity.Possible.nonNA,Privacy.Laws.Effective.nonNA
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 1.202 0.013
## max.R.sq.fit min.RMSE.fit min.aic.fit max.Adj.R.sq.fit max.R.sq.OOB
## 1 0.1272894 30.33442 5227.06 0.1023548 0.13277
## min.RMSE.OOB max.Adj.R.sq.OOB max.Rsquared.fit min.RMSESD.fit
## 1 29.28552 0.07621153 0.08406143 0.8388393
## max.RsquaredSD.fit
## 1 0.02781399
## [1] "myfit_mdl: exit: 2.775000 secs"# Check if other preProcess methods improve model performance
fit.models_1_chunk_df <-
myadd_chunk(fit.models_1_chunk_df, "fit.models_1_preProc", major.inc = FALSE,
label.minor = "preProc")## label step_major step_minor label_minor bgn end
## 4 fit.models_1_All.X 1 3 glm 78.232 81.018
## 5 fit.models_1_preProc 1 4 preProc 81.019 NA
## elapsed
## 4 2.786
## 5 NAmdl_id <- orderBy(get_model_sel_frmla(), glb_models_df)[1, "id"]
indepVar <- trim(unlist(strsplit(glb_models_df[glb_models_df$id == mdl_id,
"feats"], "[,]")))
method <- tail(unlist(strsplit(mdl_id, "[.]")), 1)
mdl_id_pfx <- paste0(head(unlist(strsplit(mdl_id, "[.]")), -1), collapse = ".")
if (!is.null(glbObsFitOutliers[[mdl_id_pfx]])) {
fitobs_df <- glbObsFit[!(glbObsFit[, glbFeatsId] %in%
glbObsFitOutliers[[mdl_id_pfx]]), ]
print(sprintf("Outliers removed: %d", nrow(glbObsFit) - nrow(fitobs_df)))
print(setdiff(glbObsFit[, glbFeatsId], fitobs_df[, glbFeatsId]))
} else fitobs_df <- glbObsFit
for (prePr in glb_preproc_methods) {
# The operations are applied in this order:
# Box-Cox/Yeo-Johnson transformation, centering, scaling, range, imputation, PCA, ICA then spatial sign.
ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
id.prefix=mdl_id_pfx,
type=glb_model_type, tune.df=glbMdlTuneParams,
trainControl.method="repeatedcv",
trainControl.number=glb_rcv_n_folds,
trainControl.repeats=glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method=method, train.preProcess=prePr)),
indepVar=indepVar, rsp_var=glb_rsp_var,
fit_df=fitobs_df, OOB_df=glbObsOOB)
}
# If (All|RFE).X.glm is less accurate than Low.Cor.X.glm
# check NA coefficients & filter appropriate terms in indepVar
# if (method == "glm") {
# orig_glm <- glb_models_lst[[paste0(mdl_id, ".", model_method)]]$finalModel
# orig_glm <- glb_models_lst[["All.X.glm"]]$finalModel; print(summary(orig_glm))
# orig_glm <- glb_models_lst[["RFE.X.glm"]]$finalModel; print(summary(orig_glm))
# require(car)
# vif_orig_glm <- vif(orig_glm); print(vif_orig_glm)
# # if vif errors out with "there are aliased coefficients in the model"
# alias_orig_glm <- alias(orig_glm); alias_complete_orig_glm <- (alias_orig_glm$Complete > 0); alias_complete_orig_glm <- alias_complete_orig_glm[rowSums(alias_complete_orig_glm) > 0, colSums(alias_complete_orig_glm) > 0]; print(alias_complete_orig_glm)
# print(vif_orig_glm[!is.na(vif_orig_glm) & (vif_orig_glm == Inf)])
# print(which.max(vif_orig_glm))
# print(sort(vif_orig_glm[vif_orig_glm >= 1.0e+03], decreasing=TRUE))
# glbObsFit[c(1143, 3637, 3953, 4105), c("UniqueID", "Popular", "H.P.quandary", "Headline")]
# glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.chrs.n.log", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in% grep("[HSA]\\.chrs.n.log", glb_feats_df$id, value=TRUE), ]
# all.equal(glbObsAll$S.chrs.uppr.n.log, glbObsAll$A.chrs.uppr.n.log)
# cor(glbObsAll$S.T.herald, glbObsAll$S.T.tribun)
# mydspObs(Abstract.contains="[Dd]iar", cols=("Abstract"), all=TRUE)
# subset(glb_feats_df, cor.y.abs <= glb_feats_df[glb_feats_df$id == ".rnorm", "cor.y.abs"])
# corxx_mtrx <- cor(data.matrix(glbObsAll[, setdiff(names(glbObsAll), myfind_chr_cols_df(glbObsAll))]), use="pairwise.complete.obs"); abs_corxx_mtrx <- abs(corxx_mtrx); diag(abs_corxx_mtrx) <- 0
# which.max(abs_corxx_mtrx["S.T.tribun", ])
# abs_corxx_mtrx["A.npnct08.log", "S.npnct08.log"]
# step_glm <- step(orig_glm)
# }
# Since caret does not optimize rpart well
# if (method == "rpart")
# ret_lst <- myfit_mdl(mdl_id=paste0(mdl_id_pfx, ".cp.0"), model_method=method,
# indepVar=indepVar,
# model_type=glb_model_type,
# rsp_var=glb_rsp_var,
# fit_df=glbObsFit, OOB_df=glbObsOOB,
# n_cv_folds=0, tune_models_df=data.frame(parameter="cp", min=0.0, max=0.0, by=0.1))
# User specified
# Ensure at least 2 vars in each regression; else varImp crashes
# sav_models_lst <- glb_models_lst; sav_models_df <- glb_models_df; sav_featsimp_df <- glb_featsimp_df; all.equal(sav_featsimp_df, glb_featsimp_df)
# glb_models_lst <- sav_models_lst; glb_models_df <- sav_models_df; glm_featsimp_df <- sav_featsimp_df
# easier to exclude features
# require(gdata) # needed for trim
# mdl_id <- "";
# indepVar <- head(subset(glb_models_df, grepl("All\\.X\\.", mdl_id), select=feats)
# , 1)[, "feats"]
# indepVar <- trim(unlist(strsplit(indepVar, "[,]")))
# indepVar <- setdiff(indepVar, ".rnorm")
# easier to include features
#stop(here"); sav_models_df <- glb_models_df; glb_models_df <- sav_models_df
# !_sp
# mdl_id <- "csm"; indepVar <- c(NULL
# ,"prdline.my.fctr", "prdline.my.fctr:.clusterid.fctr"
# ,"prdline.my.fctr*biddable"
# #,"prdline.my.fctr*startprice.log"
# #,"prdline.my.fctr*startprice.diff"
# ,"prdline.my.fctr*condition.fctr"
# ,"prdline.my.fctr*D.terms.post.stop.n"
# #,"prdline.my.fctr*D.terms.post.stem.n"
# ,"prdline.my.fctr*cellular.fctr"
# # ,"<feat1>:<feat2>"
# )
# for (method in glbMdlMethods) {
# ret_lst <- myfit_mdl(mdl_id=mdl_id, model_method=method,
# indepVar=indepVar,
# model_type=glb_model_type,
# rsp_var=glb_rsp_var,
# fit_df=glbObsFit, OOB_df=glbObsOOB,
# n_cv_folds=glb_rcv_n_folds, tune_models_df=glbMdlTuneParams)
# csm_mdl_id <- paste0(mdl_id, ".", method)
# csm_featsimp_df <- myget_feats_importance(glb_models_lst[[paste0(mdl_id, ".",
# method)]]); print(head(csm_featsimp_df))
# }
###
# Ntv.1.lm <- lm(reformulate(indepVar, glb_rsp_var), glbObsTrn); print(summary(Ntv.1.lm))
#glb_models_df[, "max.Accuracy.OOB", FALSE]
#varImp(glb_models_lst[["Low.cor.X.glm"]])
#orderBy(~ -Overall, varImp(glb_models_lst[["All.X.2.glm"]])$imp)
#orderBy(~ -Overall, varImp(glb_models_lst[["All.X.3.glm"]])$imp)
#glb_feats_df[grepl("npnct28", glb_feats_df$id), ]
# User specified bivariate models
# indepVar_lst <- list()
# for (feat in setdiff(names(glbObsFit),
# union(glb_rsp_var, glbFeatsExclude)))
# indepVar_lst[["feat"]] <- feat
# User specified combinatorial models
# indepVar_lst <- list()
# combn_mtrx <- combn(c("<feat1_name>", "<feat2_name>", "<featn_name>"),
# <num_feats_to_choose>)
# for (combn_ix in 1:ncol(combn_mtrx))
# #print(combn_mtrx[, combn_ix])
# indepVar_lst[[combn_ix]] <- combn_mtrx[, combn_ix]
# template for myfit_mdl
# rf is hard-coded in caret to recognize only Accuracy / Kappa evaluation metrics
# only for OOB in trainControl ?
# ret_lst <- myfit_mdl_fn(mdl_id=paste0(mdl_id_pfx, ""), model_method=method,
# indepVar=indepVar,
# rsp_var=glb_rsp_var,
# fit_df=glbObsFit, OOB_df=glbObsOOB,
# n_cv_folds=glb_rcv_n_folds, tune_models_df=glbMdlTuneParams,
# model_loss_mtrx=glbMdlMetric_terms,
# model_summaryFunction=glbMdlMetricSummaryFn,
# model_metric=glbMdlMetricSummary,
# model_metric_maximize=glbMdlMetricMaximize)
# Simplify a model
# fit_df <- glbObsFit; glb_mdl <- step(<complex>_mdl)
# Non-caret models
# rpart_area_mdl <- rpart(reformulate("Area", response=glb_rsp_var),
# data=glbObsFit, #method="class",
# control=rpart.control(cp=0.12),
# parms=list(loss=glbMdlMetric_terms))
# print("rpart_sel_wlm_mdl"); prp(rpart_sel_wlm_mdl)
#
print(glb_models_df)## id
## Max.cor.Y.rcv.1X1###glmnet Max.cor.Y.rcv.1X1###glmnet
## Max.cor.Y##rcv#rpart Max.cor.Y##rcv#rpart
## Interact.High.cor.Y##rcv#glmnet Interact.High.cor.Y##rcv#glmnet
## Low.cor.X##rcv#glmnet Low.cor.X##rcv#glmnet
## All.X##rcv#glmnet All.X##rcv#glmnet
## All.X##rcv#glm All.X##rcv#glm
## feats
## Max.cor.Y.rcv.1X1###glmnet Worry.About.Info.nonNA,Privacy.Laws.Effective.nonNA
## Max.cor.Y##rcv#rpart Worry.About.Info.nonNA,Privacy.Laws.Effective.nonNA
## Interact.High.cor.Y##rcv#glmnet Worry.About.Info.nonNA,Privacy.Laws.Effective.nonNA,Worry.About.Info.nonNA:.rownames,Worry.About.Info.nonNA:.pos
## Low.cor.X##rcv#glmnet Worry.About.Info.nonNA,Tried.Masking.Identity.nonNA,Smartphone.nonNA,Conservativeness.nonNA,Age.nonNA,Info.On.Internet.nonNA,Region.fctr,.rnorm,.pos,.pos.y,.rownames,Sex.fctr,Anonymity.Possible.nonNA,Privacy.Laws.Effective.nonNA
## All.X##rcv#glmnet Worry.About.Info.nonNA,Tried.Masking.Identity.nonNA,Smartphone.nonNA,Conservativeness.nonNA,Age.nonNA,Info.On.Internet.nonNA,Region.fctr,.rnorm,.pos,.pos.y,.rownames,Sex.fctr,Anonymity.Possible.nonNA,Privacy.Laws.Effective.nonNA
## All.X##rcv#glm Worry.About.Info.nonNA,Tried.Masking.Identity.nonNA,Smartphone.nonNA,Conservativeness.nonNA,Age.nonNA,Info.On.Internet.nonNA,Region.fctr,.rnorm,.pos,.pos.y,.rownames,Sex.fctr,Anonymity.Possible.nonNA,Privacy.Laws.Effective.nonNA
## max.nTuningRuns min.elapsedtime.everything
## Max.cor.Y.rcv.1X1###glmnet 0 1.005
## Max.cor.Y##rcv#rpart 5 1.467
## Interact.High.cor.Y##rcv#glmnet 25 1.688
## Low.cor.X##rcv#glmnet 25 1.725
## All.X##rcv#glmnet 25 1.956
## All.X##rcv#glm 1 1.202
## min.elapsedtime.final max.R.sq.fit
## Max.cor.Y.rcv.1X1###glmnet 0.010 0.1049928
## Max.cor.Y##rcv#rpart 0.007 0.1073327
## Interact.High.cor.Y##rcv#glmnet 0.006 0.1056166
## Low.cor.X##rcv#glmnet 0.006 0.1093654
## All.X##rcv#glmnet 0.006 0.1093654
## All.X##rcv#glm 0.013 0.1272894
## min.RMSE.fit max.Adj.R.sq.fit max.R.sq.OOB
## Max.cor.Y.rcv.1X1###glmnet 29.76034 0.10166560 0.1490914
## Max.cor.Y##rcv#rpart 29.91502 NA 0.1353391
## Interact.High.cor.Y##rcv#glmnet 29.98750 0.09894209 0.1437998
## Low.cor.X##rcv#glmnet 30.04518 0.08217045 0.1346295
## All.X##rcv#glmnet 30.04518 0.08217045 0.1346295
## All.X##rcv#glm 30.33442 0.10235479 0.1327700
## min.RMSE.OOB max.Adj.R.sq.OOB
## Max.cor.Y.rcv.1X1###glmnet 29.00863 0.14208807
## Max.cor.Y##rcv#rpart 29.24211 NA
## Interact.High.cor.Y##rcv#glmnet 29.09869 0.12958901
## Low.cor.X##rcv#glmnet 29.25410 0.07416698
## All.X##rcv#glmnet 29.25410 0.07416698
## All.X##rcv#glm 29.28552 0.07621153
## max.Rsquared.fit min.RMSESD.fit
## Max.cor.Y.rcv.1X1###glmnet NA NA
## Max.cor.Y##rcv#rpart 0.10341433 0.8129320
## Interact.High.cor.Y##rcv#glmnet 0.10132548 0.8194642
## Low.cor.X##rcv#glmnet 0.09617820 0.6984881
## All.X##rcv#glmnet 0.09617820 0.6984881
## All.X##rcv#glm 0.08406143 0.8388393
## max.RsquaredSD.fit min.aic.fit
## Max.cor.Y.rcv.1X1###glmnet NA NA
## Max.cor.Y##rcv#rpart 0.04284193 NA
## Interact.High.cor.Y##rcv#glmnet 0.04849231 NA
## Low.cor.X##rcv#glmnet 0.03771691 NA
## All.X##rcv#glmnet 0.03771691 NA
## All.X##rcv#glm 0.02781399 5227.06rm(ret_lst)
fit.models_1_chunk_df <-
myadd_chunk(fit.models_1_chunk_df, "fit.models_1_end", major.inc = FALSE,
label.minor = "teardown")## label step_major step_minor label_minor bgn end
## 5 fit.models_1_preProc 1 4 preProc 81.019 81.074
## 6 fit.models_1_end 1 5 teardown 81.075 NA
## elapsed
## 5 0.055
## 6 NAglb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc = FALSE)## label step_major step_minor label_minor bgn end elapsed
## 17 fit.models 8 1 1 73.461 81.082 7.621
## 18 fit.models 8 2 2 81.083 NA NAfit.models_2_chunk_df <-
myadd_chunk(NULL, "fit.models_2_bgn", label.minor = "setup")## label step_major step_minor label_minor bgn end elapsed
## 1 fit.models_2_bgn 1 0 setup 82.559 NA NAplt_models_df <- glb_models_df[, -grep("SD|Upper|Lower", names(glb_models_df))]
for (var in grep("^min.", names(plt_models_df), value=TRUE)) {
plt_models_df[, sub("min.", "inv.", var)] <-
#ifelse(all(is.na(tmp <- plt_models_df[, var])), NA, 1.0 / tmp)
1.0 / plt_models_df[, var]
plt_models_df <- plt_models_df[ , -grep(var, names(plt_models_df))]
}
print(plt_models_df)## id
## Max.cor.Y.rcv.1X1###glmnet Max.cor.Y.rcv.1X1###glmnet
## Max.cor.Y##rcv#rpart Max.cor.Y##rcv#rpart
## Interact.High.cor.Y##rcv#glmnet Interact.High.cor.Y##rcv#glmnet
## Low.cor.X##rcv#glmnet Low.cor.X##rcv#glmnet
## All.X##rcv#glmnet All.X##rcv#glmnet
## All.X##rcv#glm All.X##rcv#glm
## feats
## Max.cor.Y.rcv.1X1###glmnet Worry.About.Info.nonNA,Privacy.Laws.Effective.nonNA
## Max.cor.Y##rcv#rpart Worry.About.Info.nonNA,Privacy.Laws.Effective.nonNA
## Interact.High.cor.Y##rcv#glmnet Worry.About.Info.nonNA,Privacy.Laws.Effective.nonNA,Worry.About.Info.nonNA:.rownames,Worry.About.Info.nonNA:.pos
## Low.cor.X##rcv#glmnet Worry.About.Info.nonNA,Tried.Masking.Identity.nonNA,Smartphone.nonNA,Conservativeness.nonNA,Age.nonNA,Info.On.Internet.nonNA,Region.fctr,.rnorm,.pos,.pos.y,.rownames,Sex.fctr,Anonymity.Possible.nonNA,Privacy.Laws.Effective.nonNA
## All.X##rcv#glmnet Worry.About.Info.nonNA,Tried.Masking.Identity.nonNA,Smartphone.nonNA,Conservativeness.nonNA,Age.nonNA,Info.On.Internet.nonNA,Region.fctr,.rnorm,.pos,.pos.y,.rownames,Sex.fctr,Anonymity.Possible.nonNA,Privacy.Laws.Effective.nonNA
## All.X##rcv#glm Worry.About.Info.nonNA,Tried.Masking.Identity.nonNA,Smartphone.nonNA,Conservativeness.nonNA,Age.nonNA,Info.On.Internet.nonNA,Region.fctr,.rnorm,.pos,.pos.y,.rownames,Sex.fctr,Anonymity.Possible.nonNA,Privacy.Laws.Effective.nonNA
## max.nTuningRuns max.R.sq.fit
## Max.cor.Y.rcv.1X1###glmnet 0 0.1049928
## Max.cor.Y##rcv#rpart 5 0.1073327
## Interact.High.cor.Y##rcv#glmnet 25 0.1056166
## Low.cor.X##rcv#glmnet 25 0.1093654
## All.X##rcv#glmnet 25 0.1093654
## All.X##rcv#glm 1 0.1272894
## max.Adj.R.sq.fit max.R.sq.OOB
## Max.cor.Y.rcv.1X1###glmnet 0.10166560 0.1490914
## Max.cor.Y##rcv#rpart NA 0.1353391
## Interact.High.cor.Y##rcv#glmnet 0.09894209 0.1437998
## Low.cor.X##rcv#glmnet 0.08217045 0.1346295
## All.X##rcv#glmnet 0.08217045 0.1346295
## All.X##rcv#glm 0.10235479 0.1327700
## max.Adj.R.sq.OOB max.Rsquared.fit
## Max.cor.Y.rcv.1X1###glmnet 0.14208807 NA
## Max.cor.Y##rcv#rpart NA 0.10341433
## Interact.High.cor.Y##rcv#glmnet 0.12958901 0.10132548
## Low.cor.X##rcv#glmnet 0.07416698 0.09617820
## All.X##rcv#glmnet 0.07416698 0.09617820
## All.X##rcv#glm 0.07621153 0.08406143
## inv.elapsedtime.everything
## Max.cor.Y.rcv.1X1###glmnet 0.9950249
## Max.cor.Y##rcv#rpart 0.6816633
## Interact.High.cor.Y##rcv#glmnet 0.5924171
## Low.cor.X##rcv#glmnet 0.5797101
## All.X##rcv#glmnet 0.5112474
## All.X##rcv#glm 0.8319468
## inv.elapsedtime.final inv.RMSE.fit
## Max.cor.Y.rcv.1X1###glmnet 100.00000 0.03360177
## Max.cor.Y##rcv#rpart 142.85714 0.03342803
## Interact.High.cor.Y##rcv#glmnet 166.66667 0.03334723
## Low.cor.X##rcv#glmnet 166.66667 0.03328321
## All.X##rcv#glmnet 166.66667 0.03328321
## All.X##rcv#glm 76.92308 0.03296585
## inv.RMSE.OOB inv.aic.fit
## Max.cor.Y.rcv.1X1###glmnet 0.03447250 NA
## Max.cor.Y##rcv#rpart 0.03419726 NA
## Interact.High.cor.Y##rcv#glmnet 0.03436581 NA
## Low.cor.X##rcv#glmnet 0.03418324 NA
## All.X##rcv#glmnet 0.03418324 NA
## All.X##rcv#glm 0.03414657 0.0001913121# print(myplot_radar(radar_inp_df=plt_models_df))
# print(myplot_radar(radar_inp_df=subset(plt_models_df,
# !(mdl_id %in% grep("random|MFO", plt_models_df$id, value=TRUE)))))
# Compute CI for <metric>SD
glb_models_df <- mutate(glb_models_df,
max.df = ifelse(max.nTuningRuns > 1, max.nTuningRuns - 1, NA),
min.sd2ci.scaler = ifelse(is.na(max.df), NA, qt(0.975, max.df)))
for (var in grep("SD", names(glb_models_df), value=TRUE)) {
# Does CI alredy exist ?
var_components <- unlist(strsplit(var, "SD"))
varActul <- paste0(var_components[1], var_components[2])
varUpper <- paste0(var_components[1], "Upper", var_components[2])
varLower <- paste0(var_components[1], "Lower", var_components[2])
if (varUpper %in% names(glb_models_df)) {
warning(varUpper, " already exists in glb_models_df")
# Assuming Lower also exists
next
}
print(sprintf("var:%s", var))
# CI is dependent on sample size in t distribution; df=n-1
glb_models_df[, varUpper] <- glb_models_df[, varActul] +
glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
glb_models_df[, varLower] <- glb_models_df[, varActul] -
glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
}## [1] "var:min.RMSESD.fit"
## [1] "var:max.RsquaredSD.fit"# Plot metrics with CI
plt_models_df <- glb_models_df[, "id", FALSE]
pltCI_models_df <- glb_models_df[, "id", FALSE]
for (var in grep("Upper", names(glb_models_df), value=TRUE)) {
var_components <- unlist(strsplit(var, "Upper"))
col_name <- unlist(paste(var_components, collapse=""))
plt_models_df[, col_name] <- glb_models_df[, col_name]
for (name in paste0(var_components[1], c("Upper", "Lower"), var_components[2]))
pltCI_models_df[, name] <- glb_models_df[, name]
}
build_statsCI_data <- function(plt_models_df) {
mltd_models_df <- melt(plt_models_df, id.vars="id")
mltd_models_df$data <- sapply(1:nrow(mltd_models_df),
function(row_ix) tail(unlist(strsplit(as.character(
mltd_models_df[row_ix, "variable"]), "[.]")), 1))
mltd_models_df$label <- sapply(1:nrow(mltd_models_df),
function(row_ix) head(unlist(strsplit(as.character(
mltd_models_df[row_ix, "variable"]),
paste0(".", mltd_models_df[row_ix, "data"]))), 1))
#print(mltd_models_df)
return(mltd_models_df)
}
mltd_models_df <- build_statsCI_data(plt_models_df)
mltdCI_models_df <- melt(pltCI_models_df, id.vars="id")
for (row_ix in 1:nrow(mltdCI_models_df)) {
for (type in c("Upper", "Lower")) {
if (length(var_components <- unlist(strsplit(
as.character(mltdCI_models_df[row_ix, "variable"]), type))) > 1) {
#print(sprintf("row_ix:%d; type:%s; ", row_ix, type))
mltdCI_models_df[row_ix, "label"] <- var_components[1]
mltdCI_models_df[row_ix, "data"] <-
unlist(strsplit(var_components[2], "[.]"))[2]
mltdCI_models_df[row_ix, "type"] <- type
break
}
}
}
wideCI_models_df <- reshape(subset(mltdCI_models_df, select=-variable),
timevar="type",
idvar=setdiff(names(mltdCI_models_df), c("type", "value", "variable")),
direction="wide")
#print(wideCI_models_df)
mrgdCI_models_df <- merge(wideCI_models_df, mltd_models_df, all.x=TRUE)
#print(mrgdCI_models_df)
# Merge stats back in if CIs don't exist
goback_vars <- c()
for (var in unique(mltd_models_df$label)) {
for (type in unique(mltd_models_df$data)) {
var_type <- paste0(var, ".", type)
# if this data is already present, next
if (var_type %in% unique(paste(mltd_models_df$label, mltd_models_df$data,
sep=".")))
next
#print(sprintf("var_type:%s", var_type))
goback_vars <- c(goback_vars, var_type)
}
}
if (length(goback_vars) > 0) {
mltd_goback_df <- build_statsCI_data(glb_models_df[, c("id", goback_vars)])
mltd_models_df <- rbind(mltd_models_df, mltd_goback_df)
}
# mltd_models_df <- merge(mltd_models_df, glb_models_df[, c("id", "model_method")],
# all.x=TRUE)
png(paste0(glbOut$pfx, "models_bar.png"), width=480*3, height=480*2)
#print(gp <- myplot_bar(mltd_models_df, "id", "value", colorcol_name="model_method") +
print(gp <- myplot_bar(df=mltd_models_df, xcol_name="id", ycol_names="value") +
geom_errorbar(data=mrgdCI_models_df,
mapping=aes(x=mdl_id, ymax=value.Upper, ymin=value.Lower), width=0.5) +
facet_grid(label ~ data, scales="free") +
theme(axis.text.x = element_text(angle = 90,vjust = 0.5)))## Warning: Removed 1 rows containing missing values (position_stack).## Warning: Removed 4 rows containing missing values (geom_errorbar).dev.off()## quartz_off_screen
## 2print(gp)## Warning: Removed 1 rows containing missing values (position_stack).
## Warning: Removed 4 rows containing missing values (geom_errorbar).
dsp_models_cols <- c("id",
glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE))
# if (glb_is_classification && glb_is_binomial)
# dsp_models_cols <- c(dsp_models_cols, "opt.prob.threshold.OOB")
print(dsp_models_df <- orderBy(get_model_sel_frmla(), glb_models_df)[, dsp_models_cols])## id min.RMSE.OOB max.R.sq.OOB
## 1 Max.cor.Y.rcv.1X1###glmnet 29.00863 0.1490914
## 3 Interact.High.cor.Y##rcv#glmnet 29.09869 0.1437998
## 2 Max.cor.Y##rcv#rpart 29.24211 0.1353391
## 4 Low.cor.X##rcv#glmnet 29.25410 0.1346295
## 5 All.X##rcv#glmnet 29.25410 0.1346295
## 6 All.X##rcv#glm 29.28552 0.1327700
## max.Adj.R.sq.fit min.RMSE.fit
## 1 0.10166560 29.76034
## 3 0.09894209 29.98750
## 2 NA 29.91502
## 4 0.08217045 30.04518
## 5 0.08217045 30.04518
## 6 0.10235479 30.33442# print(myplot_radar(radar_inp_df = dsp_models_df))
print("Metrics used for model selection:"); print(get_model_sel_frmla())## [1] "Metrics used for model selection:"## ~+min.RMSE.OOB - max.R.sq.OOB - max.Adj.R.sq.fit + min.RMSE.fit
## <environment: 0x7ff99c453e68>print(sprintf("Best model id: %s", dsp_models_df[1, "id"]))## [1] "Best model id: Max.cor.Y.rcv.1X1###glmnet"glb_get_predictions <- function(df, mdl_id, rsp_var, prob_threshold_def=NULL, verbose=FALSE) {
mdl <- glb_models_lst[[mdl_id]]
clmnNames <- mygetPredictIds(rsp_var, mdl_id)
predct_var_name <- clmnNames$value
predct_prob_var_name <- clmnNames$prob
predct_accurate_var_name <- clmnNames$is.acc
predct_error_var_name <- clmnNames$err
predct_erabs_var_name <- clmnNames$err.abs
if (glb_is_regression) {
df[, predct_var_name] <- predict(mdl, newdata=df, type="raw")
if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_var_name) +
facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
stat_smooth(method="glm"))
df[, predct_error_var_name] <- df[, predct_var_name] - df[, glb_rsp_var]
if (verbose) print(myplot_scatter(df, predct_var_name, predct_error_var_name) +
#facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
stat_smooth(method="auto"))
if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_error_var_name) +
#facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
stat_smooth(method="glm"))
df[, predct_erabs_var_name] <- abs(df[, predct_error_var_name])
if (verbose) print(head(orderBy(reformulate(c("-", predct_erabs_var_name)), df)))
df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
}
if (glb_is_classification && glb_is_binomial) {
prob_threshold <- glb_models_df[glb_models_df$id == mdl_id,
"opt.prob.threshold.OOB"]
if (is.null(prob_threshold) || is.na(prob_threshold)) {
warning("Using default probability threshold: ", prob_threshold_def)
if (is.null(prob_threshold <- prob_threshold_def))
stop("Default probability threshold is NULL")
}
df[, predct_prob_var_name] <- predict(mdl, newdata = df, type = "prob")[, 2]
df[, predct_var_name] <-
factor(levels(df[, glb_rsp_var])[
(df[, predct_prob_var_name] >=
prob_threshold) * 1 + 1], levels(df[, glb_rsp_var]))
# if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_var_name) +
# facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
# stat_smooth(method="glm"))
df[, predct_error_var_name] <- df[, predct_var_name] != df[, glb_rsp_var]
# if (verbose) print(myplot_scatter(df, predct_var_name, predct_error_var_name) +
# #facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
# stat_smooth(method="auto"))
# if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_error_var_name) +
# #facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
# stat_smooth(method="glm"))
# if prediction is a TP (true +ve), measure distance from 1.0
tp <- which((df[, predct_var_name] == df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[2]))
df[tp, predct_erabs_var_name] <- abs(1 - df[tp, predct_prob_var_name])
#rowIx <- which.max(df[tp, predct_erabs_var_name]); df[tp, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
# if prediction is a TN (true -ve), measure distance from 0.0
tn <- which((df[, predct_var_name] == df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[1]))
df[tn, predct_erabs_var_name] <- abs(0 - df[tn, predct_prob_var_name])
#rowIx <- which.max(df[tn, predct_erabs_var_name]); df[tn, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
# if prediction is a FP (flse +ve), measure distance from 0.0
fp <- which((df[, predct_var_name] != df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[2]))
df[fp, predct_erabs_var_name] <- abs(0 - df[fp, predct_prob_var_name])
#rowIx <- which.max(df[fp, predct_erabs_var_name]); df[fp, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
# if prediction is a FN (flse -ve), measure distance from 1.0
fn <- which((df[, predct_var_name] != df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[1]))
df[fn, predct_erabs_var_name] <- abs(1 - df[fn, predct_prob_var_name])
#rowIx <- which.max(df[fn, predct_erabs_var_name]); df[fn, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
if (verbose) print(head(orderBy(reformulate(c("-", predct_erabs_var_name)), df)))
df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
}
if (glb_is_classification && !glb_is_binomial) {
df[, predct_var_name] <- predict(mdl, newdata = df, type = "raw")
probCls <- predict(mdl, newdata = df, type = "prob")
df[, predct_prob_var_name] <- NA
for (cls in names(probCls)) {
mask <- (df[, predct_var_name] == cls)
df[mask, predct_prob_var_name] <- probCls[mask, cls]
}
if (verbose) print(myplot_histogram(df, predct_prob_var_name,
fill_col_name = predct_var_name))
if (verbose) print(myplot_histogram(df, predct_prob_var_name,
facet_frmla = paste0("~", glb_rsp_var)))
# if prediction is erroneous, measure predicted class prob from actual class prob
if (all(is.na(df[, glb_rsp_var]))) {
df[, predct_error_var_name] <- NA
df[, predct_erabs_var_name] <- NA
df[, predct_accurate_var_name] <- NA
} else {
df[, predct_error_var_name] <- df[, predct_var_name] != df[, glb_rsp_var]
df[, predct_erabs_var_name] <- 0
for (cls in names(probCls)) {
mask <- (df[, glb_rsp_var] == cls) & (df[, predct_error_var_name])
df[mask, predct_erabs_var_name] <- probCls[mask, cls]
}
df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
}
}
return(df)
}
#stop(here"); glb2Sav(); glbObsAll <- savObsAll; glbObsTrn <- savObsTrn; glbObsFit <- savObsFit; glbObsOOB <- savObsOOB; sav_models_df <- glb_models_df; glb_models_df <- sav_models_df; glb_featsimp_df <- sav_featsimp_df
myget_category_stats <- function(obs_df, mdl_id, label) {
require(dplyr)
require(lazyeval)
predct_var_name <- mygetPredictIds(glb_rsp_var, mdl_id)$value
predct_error_var_name <- mygetPredictIds(glb_rsp_var, mdl_id)$err.abs
if (!predct_var_name %in% names(obs_df))
obs_df <- glb_get_predictions(obs_df, mdl_id, glb_rsp_var)
tmp_obs_df <- obs_df[, c(glbFeatsCategory, glb_rsp_var,
predct_var_name, predct_error_var_name)]
# tmp_obs_df <- obs_df %>%
# dplyr::select_(glbFeatsCategory, glb_rsp_var, predct_var_name, predct_error_var_name)
#dplyr::rename(startprice.log10.predict.RFE.X.glmnet.err=error_abs_OOB)
names(tmp_obs_df)[length(names(tmp_obs_df))] <- paste0("err.abs.", label)
ret_ctgry_df <- tmp_obs_df %>%
dplyr::group_by_(glbFeatsCategory) %>%
dplyr::summarise_(#interp(~sum(abs(var)), var=as.name(glb_rsp_var)),
interp(~sum(var), var=as.name(paste0("err.abs.", label))),
interp(~mean(var), var=as.name(paste0("err.abs.", label))),
interp(~n()))
names(ret_ctgry_df) <- c(glbFeatsCategory,
#paste0(glb_rsp_var, ".abs.", label, ".sum"),
paste0("err.abs.", label, ".sum"),
paste0("err.abs.", label, ".mean"),
paste0(".n.", label))
ret_ctgry_df <- dplyr::ungroup(ret_ctgry_df)
#colSums(ret_ctgry_df[, -grep(glbFeatsCategory, names(ret_ctgry_df))])
return(ret_ctgry_df)
}
#print(colSums((ctgry_df <- myget_category_stats(obs_df=glbObsFit, mdl_id="", label="fit"))[, -grep(glbFeatsCategory, names(ctgry_df))]))
if (!is.null(glb_mdl_ensemble)) {
fit.models_2_chunk_df <- myadd_chunk(fit.models_2_chunk_df,
paste0("fit.models_2_", mdl_id_pfx), major.inc = TRUE,
label.minor = "ensemble")
mdl_id_pfx <- "Ensemble"
if (#(glb_is_regression) |
((glb_is_classification) & (!glb_is_binomial)))
stop("Ensemble models not implemented yet for multinomial classification")
mygetEnsembleAutoMdlIds <- function() {
tmp_models_df <- orderBy(get_model_sel_frmla(), glb_models_df)
row.names(tmp_models_df) <- tmp_models_df$id
mdl_threshold_pos <-
min(which(grepl("MFO|Random|Baseline", tmp_models_df$id))) - 1
mdlIds <- tmp_models_df$id[1:mdl_threshold_pos]
return(mdlIds[!grepl("Ensemble", mdlIds)])
}
if (glb_mdl_ensemble == "auto") {
glb_mdl_ensemble <- mygetEnsembleAutoMdlIds()
mdl_id_pfx <- paste0(mdl_id_pfx, ".auto")
} else if (grepl("^%<d-%", glb_mdl_ensemble)) {
glb_mdl_ensemble <- eval(parse(text =
str_trim(unlist(strsplit(glb_mdl_ensemble, "%<d-%"))[2])))
}
for (mdl_id in glb_mdl_ensemble) {
if (!(mdl_id %in% names(glb_models_lst))) {
warning("Model ", mdl_id, " in glb_model_ensemble not found !")
next
}
glbObsFit <- glb_get_predictions(df = glbObsFit, mdl_id, glb_rsp_var)
glbObsOOB <- glb_get_predictions(df = glbObsOOB, mdl_id, glb_rsp_var)
}
#mdl_id_pfx <- "Ensemble.RFE"; mdlId <- paste0(mdl_id_pfx, ".glmnet")
#glb_mdl_ensemble <- gsub(mygetPredictIds$value, "", grep("RFE\\.X\\.(?!Interact)", row.names(glb_featsimp_df), perl = TRUE, value = TRUE), fixed = TRUE)
#varImp(glb_models_lst[[mdlId]])
#cor_df <- data.frame(cor=cor(glbObsFit[, glb_rsp_var], glbObsFit[, paste(mygetPredictIds$value, glb_mdl_ensemble)], use="pairwise.complete.obs"))
#glbObsFit <- glb_get_predictions(df=glbObsFit, "Ensemble.glmnet", glb_rsp_var);print(colSums((ctgry_df <- myget_category_stats(obs_df=glbObsFit, mdl_id="Ensemble.glmnet", label="fit"))[, -grep(glbFeatsCategory, names(ctgry_df))]))
### bid0_sp
# Better than MFO; models.n=28; min.RMSE.fit=0.0521233; err.abs.fit.sum=7.3631895
# old: Top x from auto; models.n= 5; min.RMSE.fit=0.06311047; err.abs.fit.sum=9.5937080
# RFE only ; models.n=16; min.RMSE.fit=0.05148588; err.abs.fit.sum=7.2875091
# RFE subset only ;models.n= 5; min.RMSE.fit=0.06040702; err.abs.fit.sum=9.059088
# RFE subset only ;models.n= 9; min.RMSE.fit=0.05933167; err.abs.fit.sum=8.7421288
# RFE subset only ;models.n=15; min.RMSE.fit=0.0584607; err.abs.fit.sum=8.5902066
# RFE subset only ;models.n=17; min.RMSE.fit=0.05496899; err.abs.fit.sum=8.0170431
# RFE subset only ;models.n=18; min.RMSE.fit=0.05441577; err.abs.fit.sum=7.837223
# RFE subset only ;models.n=16; min.RMSE.fit=0.05441577; err.abs.fit.sum=7.837223
### bid0_sp
### bid1_sp
# "auto"; err.abs.fit.sum=76.699774; min.RMSE.fit=0.2186429
# "RFE.X.*"; err.abs.fit.sum=; min.RMSE.fit=0.221114
### bid1_sp
indepVar <- paste(mygetPredictIds(glb_rsp_var)$value, glb_mdl_ensemble, sep = "")
if (glb_is_classification)
indepVar <- paste(indepVar, ".prob", sep = "")
# Some models in glb_mdl_ensemble might not be fitted e.g. RFE.X.Interact
indepVar <- intersect(indepVar, names(glbObsFit))
# indepVar <- grep(mygetPredictIds(glb_rsp_var)$value, names(glbObsFit), fixed=TRUE, value=TRUE)
# if (glb_is_regression)
# indepVar <- indepVar[!grepl("(err\\.abs|accurate)$", indepVar)]
# if (glb_is_classification && glb_is_binomial)
# indepVar <- grep("prob$", indepVar, value=TRUE) else
# indepVar <- indepVar[!grepl("err$", indepVar)]
#rfe_fit_ens_results <- myrun_rfe(glbObsFit, indepVar)
for (method in c("glm", "glmnet")) {
for (trainControlMethod in
c("boot", "boot632", "cv", "repeatedcv"
#, "LOOCV" # tuneLength * nrow(fitDF)
, "LGOCV", "adaptive_cv"
#, "adaptive_boot" #error: adaptive$min should be less than 3
#, "adaptive_LGOCV" #error: adaptive$min should be less than 3
)) {
#sav_models_df <- glb_models_df; all.equal(sav_models_df, glb_models_df)
#glb_models_df <- sav_models_df; print(glb_models_df$id)
if ((method == "glm") && (trainControlMethod != "repeatedcv"))
# glm used only to identify outliers
next
ret_lst <- myfit_mdl(
mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = paste0(mdl_id_pfx, ".", trainControlMethod),
type = glb_model_type, tune.df = NULL,
trainControl.method = trainControlMethod,
trainControl.number = glb_rcv_n_folds,
trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = method)),
indepVar = indepVar, rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
}
dsp_models_df <- get_dsp_models_df()
}
if (is.null(glb_sel_mdl_id))
glb_sel_mdl_id <- dsp_models_df[1, "id"] else
print(sprintf("User specified selection: %s", glb_sel_mdl_id)) ## [1] "User specified selection: All.X##rcv#glmnet"myprint_mdl(glb_sel_mdl <- glb_models_lst[[glb_sel_mdl_id]])
## Length Class Mode
## a0 69 -none- numeric
## beta 1104 dgCMatrix S4
## df 69 -none- numeric
## dim 2 -none- numeric
## lambda 69 -none- numeric
## dev.ratio 69 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 16 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 1 -none- logical
## [1] "min lambda > lambdaOpt:"
## (Intercept) Anonymity.Possible.nonNA
## 58.0050850 -1.5358027
## Privacy.Laws.Effective.nonNA Sex.fctrMale
## -5.0124203 -1.8472409
## Smartphone.nonNA Tried.Masking.Identity.nonNA
## 0.6342123 2.3713975
## Worry.About.Info.nonNA
## 14.2106322
## [1] "max lambda < lambdaOpt:"
## (Intercept) .rownames
## 57.9734929002 -0.0001035325
## Anonymity.Possible.nonNA Privacy.Laws.Effective.nonNA
## -1.7465342054 -5.2684828482
## Sex.fctrMale Smartphone.nonNA
## -2.0623865283 0.8898792979
## Tried.Masking.Identity.nonNA Worry.About.Info.nonNA
## 2.6040929372 14.4798655627## [1] TRUE# From here to save(), this should all be in one function
# these are executed in the same seq twice more:
# fit.data.training & predict.data.new chunks
print(sprintf("%s fit prediction diagnostics:", glb_sel_mdl_id))## [1] "All.X##rcv#glmnet fit prediction diagnostics:"glbObsFit <- glb_get_predictions(df = glbObsFit, mdl_id = glb_sel_mdl_id,
rsp_var = glb_rsp_var)
print(sprintf("%s OOB prediction diagnostics:", glb_sel_mdl_id))## [1] "All.X##rcv#glmnet OOB prediction diagnostics:"glbObsOOB <- glb_get_predictions(df = glbObsOOB, mdl_id = glb_sel_mdl_id,
rsp_var = glb_rsp_var)
print(glb_featsimp_df <- myget_feats_importance(mdl = glb_sel_mdl, featsimp_df = NULL))## All.X..rcv.glmnet.imp imp
## Worry.About.Info.nonNA 1.000000e+02 1.000000e+02
## Privacy.Laws.Effective.nonNA 3.542580e+01 3.542580e+01
## Tried.Masking.Identity.nonNA 1.686637e+01 1.686637e+01
## Sex.fctrMale 1.317063e+01 1.317063e+01
## Anonymity.Possible.nonNA 1.098046e+01 1.098046e+01
## Smartphone.nonNA 4.695062e+00 4.695062e+00
## .rownames 9.863266e-05 9.863266e-05
## .pos 0.000000e+00 0.000000e+00
## .pos.y 0.000000e+00 0.000000e+00
## .rnorm 0.000000e+00 0.000000e+00
## Age.nonNA 0.000000e+00 0.000000e+00
## Conservativeness.nonNA 0.000000e+00 0.000000e+00
## Info.On.Internet.nonNA 0.000000e+00 0.000000e+00
## Region.fctrMidwest 0.000000e+00 0.000000e+00
## Region.fctrNortheast 0.000000e+00 0.000000e+00
## Region.fctrWest 0.000000e+00 0.000000e+00#mdl_id <-"RFE.X.glmnet"; glb_featsimp_df <- myget_feats_importance(glb_models_lst[[mdl_id]], glb_featsimp_df); glb_featsimp_df[, paste0(mdl_id, ".imp")] <- glb_featsimp_df$imp; print(glb_featsimp_df)
#print(head(sbst_featsimp_df <- subset(glb_featsimp_df, is.na(RFE.X.glmnet.imp) | (abs(RFE.X.YeoJohnson.glmnet.imp - RFE.X.glmnet.imp) > 0.0001), select=-imp)))
#print(orderBy(~ -cor.y.abs, subset(glb_feats_df, id %in% c(row.names(sbst_featsimp_df), "startprice.dcm1.is9", "D.weight.post.stop.sum"))))
# Used again in fit.data.training & predict.data.new chunks
glb_analytics_diag_plots <- function(obs_df, mdl_id, prob_threshold=NULL) {
if (!is.null(featsimp_df <- glb_featsimp_df)) {
featsimp_df$feat <- gsub("`(.*?)`", "\\1", row.names(featsimp_df))
featsimp_df$feat.interact <- gsub("(.*?):(.*)", "\\2", featsimp_df$feat)
featsimp_df$feat <- gsub("(.*?):(.*)", "\\1", featsimp_df$feat)
featsimp_df$feat.interact <-
ifelse(featsimp_df$feat.interact == featsimp_df$feat,
NA, featsimp_df$feat.interact)
featsimp_df$feat <-
gsub("(.*?)\\.fctr(.*)", "\\1\\.fctr", featsimp_df$feat)
featsimp_df$feat.interact <-
gsub("(.*?)\\.fctr(.*)", "\\1\\.fctr", featsimp_df$feat.interact)
featsimp_df <- orderBy(~ -imp.max,
summaryBy(imp ~ feat + feat.interact, data=featsimp_df,
FUN=max))
#rex_str=":(.*)"; txt_vctr=tail(featsimp_df$feat); ret_lst <- regexec(rex_str, txt_vctr); ret_lst <- regmatches(txt_vctr, ret_lst); ret_vctr <- sapply(1:length(ret_lst), function(pos_ix) ifelse(length(ret_lst[[pos_ix]]) > 0, ret_lst[[pos_ix]], "")); print(ret_vctr <- ret_vctr[ret_vctr != ""])
featsimp_df <- subset(featsimp_df, !is.na(imp.max))
if (nrow(featsimp_df) > 5) {
warning("Limiting important feature scatter plots to 5 out of ",
nrow(featsimp_df))
featsimp_df <- head(featsimp_df, 5)
}
# if (!all(is.na(featsimp_df$feat.interact)))
# stop("not implemented yet")
rsp_var_out <- mygetPredictIds(glb_rsp_var, mdl_id)$value
for (var in featsimp_df$feat) {
plot_df <- melt(obs_df, id.vars = var,
measure.vars = c(glb_rsp_var, rsp_var_out))
print(myplot_scatter(plot_df, var, "value", colorcol_name = "variable",
facet_colcol_name = "variable", jitter = TRUE) +
guides(color = FALSE))
}
}
if (glb_is_regression) {
if (is.null(featsimp_df) || (nrow(featsimp_df) == 0))
warning("No important features in glb_fin_mdl") else
print(myplot_prediction_regression(df=obs_df,
feat_x=ifelse(nrow(featsimp_df) > 1, featsimp_df$feat[2],
".rownames"),
feat_y=featsimp_df$feat[1],
rsp_var=glb_rsp_var, rsp_var_out=rsp_var_out,
id_vars=glbFeatsId)
# + facet_wrap(reformulate(featsimp_df$feat[2])) # if [1 or 2] is a factor
# + geom_point(aes_string(color="<col_name>.fctr")) # to color the plot
)
}
if (glb_is_classification) {
if (is.null(featsimp_df) || (nrow(featsimp_df) == 0))
warning("No features in selected model are statistically important")
else print(myplot_prediction_classification(df = obs_df,
feat_x = ifelse(nrow(featsimp_df) > 1,
featsimp_df$feat[2], ".rownames"),
feat_y = featsimp_df$feat[1],
rsp_var = glb_rsp_var,
rsp_var_out = rsp_var_out,
id_vars = glbFeatsId,
prob_threshold = prob_threshold))
}
}
if (glb_is_classification && glb_is_binomial)
glb_analytics_diag_plots(obs_df = glbObsOOB, mdl_id = glb_sel_mdl_id,
prob_threshold = glb_models_df[glb_models_df$id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"]) else
glb_analytics_diag_plots(obs_df = glbObsOOB, mdl_id = glb_sel_mdl_id) ## Warning in glb_analytics_diag_plots(obs_df = glbObsOOB, mdl_id =
## glb_sel_mdl_id): Limiting important feature scatter plots to 5 out of 14
## Internet.Use Smartphone Sex Age State Region
## 277 0 1 Female 69 California West
## 485 1 1 Male 30 California West
## 457 1 1 Female 21 Texas South
## 509 1 1 Male 28 New Jersey Northeast
## 756 1 1 Male 50 California West
## Conservativeness Info.On.Internet Worry.About.Info Privacy.Importance
## 277 3 0 1 0.00000
## 485 3 3 1 0.00000
## 457 3 9 1 6.25000
## 509 1 2 1 11.11111
## 756 4 1 1 12.50000
## Anonymity.Possible Tried.Masking.Identity Privacy.Laws.Effective .src
## 277 0 0 0 Train
## 485 1 0 0 Train
## 457 0 0 0 Train
## 509 1 1 0 Train
## 756 0 0 0 Train
## .rnorm .pos .pos.y .rownames .category Sex.fctr Region.fctr
## 277 -0.69662752 277 277 400 .dummy Female West
## 485 0.11078702 485 485 653 .dummy Male West
## 457 1.60633241 457 457 623 .dummy Female South
## 509 -1.01882446 509 509 681 .dummy Male Northeast
## 756 0.08752588 756 756 967 .dummy Male West
## Internet.Use.nonNA Smartphone.nonNA Age.nonNA Conservativeness.nonNA
## 277 0 1 69 3
## 485 1 1 30 3
## 457 1 1 21 3
## 509 1 1 28 1
## 756 1 1 50 4
## Info.On.Internet.nonNA Worry.About.Info.nonNA Anonymity.Possible.nonNA
## 277 0 1 0
## 485 3 1 1
## 457 9 1 0
## 509 2 1 1
## 756 1 1 0
## Tried.Masking.Identity.nonNA Privacy.Laws.Effective.nonNA
## 277 0 0
## 485 0 0
## 457 0 0
## 509 1 0
## 756 0 0
## Privacy.Importance.All.X..rcv.glmnet
## 277 72.91126
## 485 69.46686
## 457 72.90813
## 509 71.86945
## 756 71.02685
## Privacy.Importance.All.X..rcv.glmnet.err
## 277 72.91126
## 485 69.46686
## 457 66.65813
## 509 60.75834
## 756 58.52685
## Privacy.Importance.All.X..rcv.glmnet.err.abs
## 277 72.91126
## 485 69.46686
## 457 66.65813
## 509 60.75834
## 756 58.52685
## Privacy.Importance.All.X..rcv.glmnet.is.acc .label
## 277 FALSE 400
## 485 FALSE 653
## 457 FALSE 623
## 509 FALSE 681
## 756 FALSE 967
if (!is.null(glbFeatsCategory)) {
glbLvlCategory <- merge(glbLvlCategory,
myget_category_stats(obs_df = glbObsFit, mdl_id = glb_sel_mdl_id,
label = "fit"),
by = glbFeatsCategory, all = TRUE)
row.names(glbLvlCategory) <- glbLvlCategory[, glbFeatsCategory]
glbLvlCategory <- merge(glbLvlCategory,
myget_category_stats(obs_df = glbObsOOB, mdl_id = glb_sel_mdl_id,
label="OOB"),
#by=glbFeatsCategory, all=TRUE) glb_ctgry-df already contains .n.OOB ?
all = TRUE)
row.names(glbLvlCategory) <- glbLvlCategory[, glbFeatsCategory]
if (any(grepl("OOB", glbMdlMetricsEval)))
print(orderBy(~-err.abs.OOB.mean, glbLvlCategory)) else
print(orderBy(~-err.abs.fit.mean, glbLvlCategory))
print(colSums(glbLvlCategory[, -grep(glbFeatsCategory, names(glbLvlCategory))]))
}## .category .n.OOB .n.Fit .n.Tst .freqRatio.Fit .freqRatio.OOB
## .dummy .dummy 246 541 215 1 1
## .freqRatio.Tst err.abs.fit.sum err.abs.fit.mean .n.fit
## .dummy 1 13674.32 25.276 541
## err.abs.OOB.sum err.abs.OOB.mean
## .dummy 6109.817 24.83665
## .n.OOB .n.Fit .n.Tst .freqRatio.Fit
## 246.00000 541.00000 215.00000 1.00000
## .freqRatio.OOB .freqRatio.Tst err.abs.fit.sum err.abs.fit.mean
## 1.00000 1.00000 13674.31778 25.27600
## .n.fit err.abs.OOB.sum err.abs.OOB.mean
## 541.00000 6109.81688 24.83665write.csv(glbObsOOB[, c(glbFeatsId,
grep(glb_rsp_var, names(glbObsOOB), fixed=TRUE, value=TRUE))],
paste0(gsub(".", "_", paste0(glbOut$pfx, glb_sel_mdl_id), fixed=TRUE),
"_OOBobs.csv"), row.names=FALSE)
fit.models_2_chunk_df <-
myadd_chunk(NULL, "fit.models_2_bgn", label.minor = "teardown")## label step_major step_minor label_minor bgn end elapsed
## 1 fit.models_2_bgn 1 0 teardown 88.946 NA NAglb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)## label step_major step_minor label_minor bgn end elapsed
## 18 fit.models 8 2 2 81.083 88.956 7.873
## 19 fit.models 8 3 3 88.956 NA NA# if (sum(is.na(glbObsAll$D.P.http)) > 0)
# stop("fit.models_3: Why is this happening ?")
#stop(here"); glb2Sav()
sync_glb_obs_df <- function() {
# Merge or cbind ?
for (col in setdiff(names(glbObsFit), names(glbObsTrn)))
glbObsTrn[glbObsTrn$.lcn == "Fit", col] <<- glbObsFit[, col]
for (col in setdiff(names(glbObsFit), names(glbObsAll)))
glbObsAll[glbObsAll$.lcn == "Fit", col] <<- glbObsFit[, col]
if (all(is.na(glbObsNew[, glb_rsp_var])))
for (col in setdiff(names(glbObsOOB), names(glbObsTrn)))
glbObsTrn[glbObsTrn$.lcn == "OOB", col] <<- glbObsOOB[, col]
for (col in setdiff(names(glbObsOOB), names(glbObsAll)))
glbObsAll[glbObsAll$.lcn == "OOB", col] <<- glbObsOOB[, col]
}
sync_glb_obs_df()
print(setdiff(names(glbObsNew), names(glbObsAll)))## character(0)replay.petrisim(pn = glb_analytics_pn,
replay.trans = (glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"model.selected")), flip_coord = TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
## 2.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction firing: model.selected
## 3.0000 3 0 2 1 0
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc = TRUE)## label step_major step_minor label_minor bgn end elapsed
## 19 fit.models 8 3 3 88.956 92.8 3.844
## 20 fit.data.training 9 0 0 92.801 NA NAStep 9.0: fit data training
#load(paste0(glb_inp_pfx, "dsk.RData"))
if (!is.null(glb_fin_mdl_id) && (glb_fin_mdl_id %in% names(glb_models_lst))) {
warning("Final model same as user selected model")
glb_fin_mdl <- glb_models_lst[[glb_fin_mdl_id]]
} else
# if (nrow(glbObsFit) + length(glbObsFitOutliers) == nrow(glbObsTrn))
if (!all(is.na(glbObsNew[, glb_rsp_var])))
{
warning("Final model same as glb_sel_mdl_id")
glb_fin_mdl_id <- paste0("Final.", glb_sel_mdl_id)
glb_fin_mdl <- glb_sel_mdl
glb_models_lst[[glb_fin_mdl_id]] <- glb_fin_mdl
mdlDf <- glb_models_df[glb_models_df$id == glb_sel_mdl_id, ]
mdlDf$id <- glb_fin_mdl_id
glb_models_df <- rbind(glb_models_df, mdlDf)
} else {
if (grepl("RFE\\.X", names(glbMdlFamilies))) {
indepVar <- mygetIndepVar(glb_feats_df)
rfe_trn_results <-
myrun_rfe(glbObsTrn, indepVar, glbRFESizes[["Final"]])
if (!isTRUE(all.equal(sort(predictors(rfe_trn_results)),
sort(predictors(rfe_fit_results))))) {
print("Diffs predictors(rfe_trn_results) vs. predictors(rfe_fit_results):")
print(setdiff(predictors(rfe_trn_results), predictors(rfe_fit_results)))
print("Diffs predictors(rfe_fit_results) vs. predictors(rfe_trn_results):")
print(setdiff(predictors(rfe_fit_results), predictors(rfe_trn_results)))
}
}
# }
if (grepl("Ensemble", glb_sel_mdl_id)) {
# Find which models are relevant
mdlimp_df <- subset(myget_feats_importance(glb_sel_mdl), imp > 5)
# Fit selected models on glbObsTrn
for (mdl_id in gsub(".prob", "",
gsub(mygetPredictIds(glb_rsp_var)$value, "", row.names(mdlimp_df), fixed = TRUE),
fixed = TRUE)) {
mdl_id_components <- unlist(strsplit(mdl_id, "[.]"))
mdlIdPfx <- paste0(c(head(mdl_id_components, -1), "Train"),
collapse = ".")
if (grepl("RFE\\.X\\.", mdlIdPfx))
mdlIndepVars <- myadjustInteractionFeats(glb_feats_df, myextract_actual_feats(
predictors(rfe_trn_results))) else
mdlIndepVars <- trim(unlist(
strsplit(glb_models_df[glb_models_df$id == mdl_id, "feats"], "[,]")))
ret_lst <-
myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = mdlIdPfx,
type = glb_model_type, tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds,
trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = tail(mdl_id_components, 1))),
indepVar = mdlIndepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsTrn, OOB_df = NULL)
glbObsTrn <- glb_get_predictions(df = glbObsTrn,
mdl_id = tail(glb_models_df$id, 1),
rsp_var = glb_rsp_var,
prob_threshold_def =
subset(glb_models_df, id == mdl_id)$opt.prob.threshold.OOB)
glbObsNew <- glb_get_predictions(df = glbObsNew,
mdl_id = tail(glb_models_df$id, 1),
rsp_var = glb_rsp_var,
prob_threshold_def =
subset(glb_models_df, id == mdl_id)$opt.prob.threshold.OOB)
}
}
# "Final" model
if ((model_method <- glb_sel_mdl$method) == "custom")
# get actual method from the mdl_id
model_method <- tail(unlist(strsplit(glb_sel_mdl_id, "[.]")), 1)
if (grepl("Ensemble", glb_sel_mdl_id)) {
# Find which models are relevant
mdlimp_df <- subset(myget_feats_importance(glb_sel_mdl), imp > 5)
if (glb_is_classification && glb_is_binomial)
indepVar <- gsub("(.*)\\.(.*)\\.prob", "\\1\\.Train\\.\\2\\.prob",
row.names(mdlimp_df)) else
indepVar <- gsub("(.*)\\.(.*)", "\\1\\.Train\\.\\2",
row.names(mdlimp_df))
} else
if (grepl("RFE.X", glb_sel_mdl_id, fixed = TRUE)) {
indepVar <- myextract_actual_feats(predictors(rfe_trn_results))
} else indepVar <-
trim(unlist(strsplit(glb_models_df[glb_models_df$id ==
glb_sel_mdl_id
, "feats"], "[,]")))
if (!is.null(glb_preproc_methods) &&
((match_pos <- regexpr(gsub(".", "\\.",
paste(glb_preproc_methods, collapse = "|"),
fixed = TRUE), glb_sel_mdl_id)) != -1))
ths_preProcess <- str_sub(glb_sel_mdl_id, match_pos,
match_pos + attr(match_pos, "match.length") - 1) else
ths_preProcess <- NULL
mdl_id_pfx <- ifelse(grepl("Ensemble", glb_sel_mdl_id),
"Final.Ensemble", "Final")
trnobs_df <- glbObsTrn
if (!is.null(glbObsTrnOutliers[[mdl_id_pfx]])) {
trnobs_df <- glbObsTrn[!(glbObsTrn[, glbFeatsId] %in% glbObsTrnOutliers[[mdl_id_pfx]]), ]
print(sprintf("Outliers removed: %d", nrow(glbObsTrn) - nrow(trnobs_df)))
print(setdiff(glbObsTrn[, glbFeatsId], trnobs_df[, glbFeatsId]))
}
# Force fitting of Final.glm to identify outliers
method_vctr <- unique(c(myparseMdlId(glb_sel_mdl_id)$alg, glbMdlFamilies[["Final"]]))
for (method in method_vctr) {
#source("caret_nominalTrainWorkflow.R")
# glmnet requires at least 2 indep vars
if ((length(indepVar) == 1) && (method %in% "glmnet"))
next
ret_lst <-
myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = mdl_id_pfx,
type = glb_model_type, trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds,
trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = method,
train.preProcess = ths_preProcess)),
indepVar = indepVar, rsp_var = glb_rsp_var,
fit_df = trnobs_df, OOB_df = NULL)
if ((length(method_vctr) == 1) || (method != "glm")) {
glb_fin_mdl <- glb_models_lst[[length(glb_models_lst)]]
glb_fin_mdl_id <- glb_models_df[length(glb_models_lst), "id"]
}
}
}## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: Final##rcv#glmnet"
## [1] " indepVar: Worry.About.Info.nonNA,Tried.Masking.Identity.nonNA,Smartphone.nonNA,Conservativeness.nonNA,Age.nonNA,Info.On.Internet.nonNA,Region.fctr,.rnorm,.pos,.pos.y,.rownames,Sex.fctr,Anonymity.Possible.nonNA,Privacy.Laws.Effective.nonNA"
## [1] "myfit_mdl: setup complete: 0.747000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 1, lambda = 0.912 on full training set
## [1] "myfit_mdl: train complete: 2.934000 secs"## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: alpha
## Length Class Mode
## a0 67 -none- numeric
## beta 1072 dgCMatrix S4
## df 67 -none- numeric
## dim 2 -none- numeric
## lambda 67 -none- numeric
## dev.ratio 67 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 16 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 1 -none- logical
## [1] "min lambda > lambdaOpt:"
## (Intercept) Anonymity.Possible.nonNA
## 58.23333094 -3.34004858
## Conservativeness.nonNA Privacy.Laws.Effective.nonNA
## 0.10575773 -8.83324328
## Sex.fctrMale Smartphone.nonNA
## -1.12181317 0.07627957
## Tried.Masking.Identity.nonNA Worry.About.Info.nonNA
## 2.22461829 16.21579466
## [1] "max lambda < lambdaOpt:"
## (Intercept) Anonymity.Possible.nonNA
## 57.8762107 -3.4836708
## Conservativeness.nonNA Privacy.Laws.Effective.nonNA
## 0.2077586 -8.9975253
## Sex.fctrMale Smartphone.nonNA
## -1.3146491 0.2844564
## Tried.Masking.Identity.nonNA Worry.About.Info.nonNA
## 2.4202512 16.3327427
## [1] "myfit_mdl: train diagnostics complete: 3.525000 secs"
## [1] "myfit_mdl: predict complete: 3.598000 secs"
## id
## 1 Final##rcv#glmnet
## feats
## 1 Worry.About.Info.nonNA,Tried.Masking.Identity.nonNA,Smartphone.nonNA,Conservativeness.nonNA,Age.nonNA,Info.On.Internet.nonNA,Region.fctr,.rnorm,.pos,.pos.y,.rownames,Sex.fctr,Anonymity.Possible.nonNA,Privacy.Laws.Effective.nonNA
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 2.178 0.007
## max.R.sq.fit min.RMSE.fit max.Adj.R.sq.fit max.Rsquared.fit
## 1 0.1287631 29.62955 0.1106595 0.1149333
## min.RMSESD.fit max.RsquaredSD.fit
## 1 0.3420864 0.01848209
## [1] "myfit_mdl: exit: 3.611000 secs"rm(ret_lst)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc=FALSE)## label step_major step_minor label_minor bgn end
## 20 fit.data.training 9 0 0 92.801 96.876
## 21 fit.data.training 9 1 1 96.877 NA
## elapsed
## 20 4.076
## 21 NA#stop(here"); glb2Sav()
if (glb_is_classification && glb_is_binomial)
prob_threshold <- glb_models_df[glb_models_df$id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"] else
prob_threshold <- NULL
if (grepl("Ensemble", glb_fin_mdl_id)) {
# Get predictions for each model in ensemble; Outliers that have been moved to OOB might not have been predicted yet
mdlEnsembleComps <- unlist(str_split(subset(glb_models_df,
id == glb_fin_mdl_id)$feats, ","))
if (glb_is_classification && glb_is_binomial)
mdlEnsembleComps <- gsub("\\.prob$", "", mdlEnsembleComps)
mdlEnsembleComps <- gsub(paste0("^",
gsub(".", "\\.", mygetPredictIds(glb_rsp_var)$value, fixed = TRUE)),
"", mdlEnsembleComps)
for (mdl_id in mdlEnsembleComps) {
glbObsTrn <- glb_get_predictions(df = glbObsTrn, mdl_id = mdl_id,
rsp_var = glb_rsp_var,
prob_threshold_def = prob_threshold)
glbObsNew <- glb_get_predictions(df = glbObsNew, mdl_id = mdl_id,
rsp_var = glb_rsp_var,
prob_threshold_def = prob_threshold)
}
}
glbObsTrn <- glb_get_predictions(df = glbObsTrn, mdl_id = glb_fin_mdl_id,
rsp_var = glb_rsp_var,
prob_threshold_def = prob_threshold)
glb_featsimp_df <- myget_feats_importance(mdl=glb_fin_mdl,
featsimp_df=glb_featsimp_df)
#glb_featsimp_df[, paste0(glb_fin_mdl_id, ".imp")] <- glb_featsimp_df$imp
print(glb_featsimp_df)## All.X..rcv.glmnet.imp Final..rcv.glmnet.imp
## Worry.About.Info.nonNA 1.000000e+02 100.000000
## Privacy.Laws.Effective.nonNA 3.542580e+01 54.819637
## Anonymity.Possible.nonNA 1.098046e+01 21.009376
## Tried.Masking.Identity.nonNA 1.686637e+01 14.337640
## Sex.fctrMale 1.317063e+01 7.554599
## Smartphone.nonNA 4.695062e+00 1.185816
## Conservativeness.nonNA 0.000000e+00 1.001023
## .pos 0.000000e+00 0.000000
## .pos.y 0.000000e+00 0.000000
## .rnorm 0.000000e+00 0.000000
## .rownames 9.863266e-05 0.000000
## Age.nonNA 0.000000e+00 0.000000
## Info.On.Internet.nonNA 0.000000e+00 0.000000
## Region.fctrMidwest 0.000000e+00 0.000000
## Region.fctrNortheast 0.000000e+00 0.000000
## Region.fctrWest 0.000000e+00 0.000000
## imp
## Worry.About.Info.nonNA 100.000000
## Privacy.Laws.Effective.nonNA 54.819637
## Anonymity.Possible.nonNA 21.009376
## Tried.Masking.Identity.nonNA 14.337640
## Sex.fctrMale 7.554599
## Smartphone.nonNA 1.185816
## Conservativeness.nonNA 1.001023
## .pos 0.000000
## .pos.y 0.000000
## .rnorm 0.000000
## .rownames 0.000000
## Age.nonNA 0.000000
## Info.On.Internet.nonNA 0.000000
## Region.fctrMidwest 0.000000
## Region.fctrNortheast 0.000000
## Region.fctrWest 0.000000if (glb_is_classification && glb_is_binomial)
glb_analytics_diag_plots(obs_df=glbObsTrn, mdl_id=glb_fin_mdl_id,
prob_threshold=glb_models_df[glb_models_df$id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"]) else
glb_analytics_diag_plots(obs_df=glbObsTrn, mdl_id=glb_fin_mdl_id) ## Warning in glb_analytics_diag_plots(obs_df = glbObsTrn, mdl_id =
## glb_fin_mdl_id): Limiting important feature scatter plots to 5 out of 14
## Smartphone Sex Age State Region Conservativeness
## 277 1 Female 69 California West 3
## 622 1 Female 28 Washington West 2
## 171 0 Male 66 Tennessee South 4
## 122 0 Female 71 Massachusetts Northeast 2
## 485 1 Male 30 California West 3
## Info.On.Internet Worry.About.Info Privacy.Importance
## 277 0 1 0
## 622 0 1 0
## 171 1 1 0
## 122 4 1 0
## 485 3 1 0
## Anonymity.Possible Tried.Masking.Identity Privacy.Laws.Effective .src
## 277 0 0 0 Train
## 622 0 0 NA Train
## 171 0 0 0 Train
## 122 1 0 0 Train
## 485 1 0 0 Train
## .rnorm .pos .pos.y .rownames Sex.fctr Region.fctr
## 277 -0.696627523 277 277 400 Female West
## 622 -0.300846411 622 622 810 Female West
## 171 -0.596385049 171 171 262 Male South
## 122 0.006096444 122 122 181 Female Northeast
## 485 0.110787016 485 485 653 Male West
## Smartphone.nonNA Age.nonNA Conservativeness.nonNA
## 277 1 69 3
## 622 1 28 2
## 171 0 66 4
## 122 0 71 2
## 485 1 30 3
## Info.On.Internet.nonNA Worry.About.Info.nonNA Anonymity.Possible.nonNA
## 277 0 1 0
## 622 0 1 0
## 171 1 1 0
## 122 4 1 1
## 485 3 1 1
## Tried.Masking.Identity.nonNA Privacy.Laws.Effective.nonNA .lcn
## 277 0 0 OOB
## 622 0 0 Fit
## 171 0 0 Fit
## 122 0 0 Fit
## 485 0 0 OOB
## .category Internet.Use Internet.Use.nonNA
## 277 .dummy NA NA
## 622 .dummy 1 1
## 171 .dummy 1 1
## 122 .dummy 1 1
## 485 .dummy NA NA
## Privacy.Importance.All.X..rcv.glmnet
## 277 NA
## 622 72.90550
## 171 70.36785
## 122 70.68102
## 485 NA
## Privacy.Importance.All.X..rcv.glmnet.err
## 277 NA
## 622 72.90550
## 171 70.36785
## 122 70.68102
## 485 NA
## Privacy.Importance.All.X..rcv.glmnet.err.abs
## 277 NA
## 622 72.90550
## 171 70.36785
## 122 70.68102
## 485 NA
## Privacy.Importance.All.X..rcv.glmnet.is.acc
## 277 NA
## 622 FALSE
## 171 FALSE
## 122 FALSE
## 485 NA
## Privacy.Importance.Final..rcv.glmnet
## 277 74.99640
## 622 74.83342
## 171 73.73632
## 122 71.21973
## 485 70.34578
## Privacy.Importance.Final..rcv.glmnet.err
## 277 74.99640
## 622 74.83342
## 171 73.73632
## 122 71.21973
## 485 70.34578
## Privacy.Importance.Final..rcv.glmnet.err.abs
## 277 74.99640
## 622 74.83342
## 171 73.73632
## 122 71.21973
## 485 70.34578
## Privacy.Importance.Final..rcv.glmnet.is.acc .label
## 277 FALSE 400
## 622 FALSE 810
## 171 FALSE 262
## 122 FALSE 181
## 485 FALSE 653
dsp_feats_vctr <- c(NULL)
for(var in grep(".imp", names(glb_feats_df), fixed=TRUE, value=TRUE))
dsp_feats_vctr <- union(dsp_feats_vctr,
glb_feats_df[!is.na(glb_feats_df[, var]), "id"])
# print(glbObsTrn[glbObsTrn$UniqueID %in% FN_OOB_ids,
# grep(glb_rsp_var, names(glbObsTrn), value=TRUE)])
print(setdiff(names(glbObsTrn), names(glbObsAll)))## [1] "Privacy.Importance.Final..rcv.glmnet"
## [2] "Privacy.Importance.Final..rcv.glmnet.err"
## [3] "Privacy.Importance.Final..rcv.glmnet.err.abs"
## [4] "Privacy.Importance.Final..rcv.glmnet.is.acc"for (col in setdiff(names(glbObsTrn), names(glbObsAll)))
# Merge or cbind ?
glbObsAll[glbObsAll$.src == "Train", col] <- glbObsTrn[, col]
print(setdiff(names(glbObsFit), names(glbObsAll)))## character(0)print(setdiff(names(glbObsOOB), names(glbObsAll)))## character(0)for (col in setdiff(names(glbObsOOB), names(glbObsAll)))
# Merge or cbind ?
glbObsAll[glbObsAll$.lcn == "OOB", col] <- glbObsOOB[, col]
print(setdiff(names(glbObsNew), names(glbObsAll)))## character(0)#glb2Sav(); all.equal(savObsAll, glbObsAll); all.equal(sav_models_lst, glb_models_lst)
#load(file = paste0(glbOut$pfx, "dsk_knitr.RData"))
#cmpCols <- names(glbObsAll)[!grepl("\\.Final\\.", names(glbObsAll))]; all.equal(savObsAll[, cmpCols], glbObsAll[, cmpCols]); all.equal(savObsAll[, "H.P.http"], glbObsAll[, "H.P.http"]);
replay.petrisim(pn = glb_analytics_pn,
replay.trans = (glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"data.training.all.prediction","model.final")), flip_coord = TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
## 2.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction firing: model.selected
## 3.0000 3 0 2 1 0
## 3.0000 multiple enabled transitions: model.final data.training.all.prediction data.new.prediction firing: data.training.all.prediction
## 4.0000 5 0 1 1 1
## 4.0000 multiple enabled transitions: model.final data.training.all.prediction data.new.prediction firing: model.final
## 5.0000 4 0 0 2 1
glb_chunks_df <- myadd_chunk(glb_chunks_df, "predict.data.new", major.inc = TRUE)## label step_major step_minor label_minor bgn end
## 21 fit.data.training 9 1 1 96.877 102.834
## 22 predict.data.new 10 0 0 102.835 NA
## elapsed
## 21 5.958
## 22 NAStep 10.0: predict data new
## Warning in glb_analytics_diag_plots(obs_df = glbObsNew, mdl_id =
## glb_fin_mdl_id, : Limiting important feature scatter plots to 5 out of 14## Warning: Removed 215 rows containing missing values (geom_point).
## Warning: Removed 215 rows containing missing values (geom_point).
## Warning: Removed 215 rows containing missing values (geom_point).
## Warning: Removed 215 rows containing missing values (geom_point).
## Warning: Removed 215 rows containing missing values (geom_point).
## Warning: Removed 215 rows containing missing values (geom_point).
## Warning: Removed 215 rows containing missing values (geom_point).
## Warning: Removed 215 rows containing missing values (geom_point).
## Warning: Removed 215 rows containing missing values (geom_point).
## Warning: Removed 215 rows containing missing values (geom_point).
## Smartphone Sex Age State Region Conservativeness
## 788 1 Female 70 New Jersey Northeast 4
## 789 NA Female 80 Georgia South 4
## 790 0 Female 76 New York Northeast 3
## 791 NA Male 75 North Carolina South 4
## 792 0 Male 69 Ohio Midwest 4
## Info.On.Internet Worry.About.Info Privacy.Importance
## 788 0 0 NA
## 789 NA NA NA
## 790 NA NA NA
## 791 NA NA NA
## 792 NA NA NA
## Anonymity.Possible Tried.Masking.Identity Privacy.Laws.Effective .src
## 788 0 0 NA Test
## 789 NA NA NA Test
## 790 NA NA NA Test
## 791 NA NA 0 Test
## 792 NA NA 0 Test
## .rnorm .pos .pos.y .rownames Sex.fctr Region.fctr Smartphone.nonNA
## 788 0.3429277 788 788 3 Female Northeast 1
## 789 -0.4489313 789 789 5 Female South 0
## 790 0.3191098 790 790 8 Female Northeast 0
## 791 -0.7071810 791 791 9 Male South 0
## 792 0.0857561 792 792 11 Male Midwest 0
## Age.nonNA Conservativeness.nonNA Info.On.Internet.nonNA
## 788 70 4 0
## 789 80 4 0
## 790 76 3 0
## 791 75 4 0
## 792 69 4 0
## Worry.About.Info.nonNA Anonymity.Possible.nonNA
## 788 0 0
## 789 0 1
## 790 0 0
## 791 0 0
## 792 0 0
## Tried.Masking.Identity.nonNA Privacy.Laws.Effective.nonNA .lcn
## 788 0 0
## 789 0 0
## 790 0 0
## 791 0 0
## 792 0 0
## .category Privacy.Importance.Final..rcv.glmnet
## 788 .dummy 58.87797
## 789 .dummy 55.26429
## 790 .dummy 58.52193
## 791 .dummy 57.45491
## 792 .dummy 57.45491
## Privacy.Importance.Final..rcv.glmnet.err
## 788 NA
## 789 NA
## 790 NA
## 791 NA
## 792 NA
## Privacy.Importance.Final..rcv.glmnet.err.abs
## 788 NA
## 789 NA
## 790 NA
## 791 NA
## 792 NA
## Privacy.Importance.Final..rcv.glmnet.is.acc .label
## 788 NA 3
## 789 NA 5
## 790 NA 8
## 791 NA 9
## 792 NA 11
## [1] TRUE## [1] "glb_sel_mdl_id: All.X##rcv#glmnet"## [1] "glb_fin_mdl_id: Final##rcv#glmnet"## [1] "Cross Validation issues:"
## Max.cor.Y.rcv.1X1###glmnet
## 0## min.RMSE.OOB max.R.sq.OOB max.Adj.R.sq.fit
## Max.cor.Y.rcv.1X1###glmnet 29.00863 0.1490914 0.10166560
## Interact.High.cor.Y##rcv#glmnet 29.09869 0.1437998 0.09894209
## Max.cor.Y##rcv#rpart 29.24211 0.1353391 NA
## Low.cor.X##rcv#glmnet 29.25410 0.1346295 0.08217045
## All.X##rcv#glmnet 29.25410 0.1346295 0.08217045
## All.X##rcv#glm 29.28552 0.1327700 0.10235479
## Final##rcv#glmnet NA NA 0.11065946
## min.RMSE.fit
## Max.cor.Y.rcv.1X1###glmnet 29.76034
## Interact.High.cor.Y##rcv#glmnet 29.98750
## Max.cor.Y##rcv#rpart 29.91502
## Low.cor.X##rcv#glmnet 30.04518
## All.X##rcv#glmnet 30.04518
## All.X##rcv#glm 30.33442
## Final##rcv#glmnet 29.62955## [1] "All.X##rcv#glmnet OOB RMSE: 29.2541"## err.abs.fit.sum err.abs.OOB.sum err.abs.trn.sum err.abs.new.sum
## .dummy 13674.32 6109.817 19538.7 NA
## .freqRatio.Fit .freqRatio.OOB .freqRatio.Tst .n.Fit .n.OOB .n.Tst
## .dummy 1 1 1 541 246 215
## .n.fit .n.new .n.trn err.abs.OOB.mean err.abs.fit.mean
## .dummy 541 215 787 24.83665 25.276
## err.abs.new.mean err.abs.trn.mean
## .dummy NA 24.82682
## err.abs.fit.sum err.abs.OOB.sum err.abs.trn.sum err.abs.new.sum
## 13674.31778 6109.81688 19538.70447 NA
## .freqRatio.Fit .freqRatio.OOB .freqRatio.Tst .n.Fit
## 1.00000 1.00000 1.00000 541.00000
## .n.OOB .n.Tst .n.fit .n.new
## 246.00000 215.00000 541.00000 215.00000
## .n.trn err.abs.OOB.mean err.abs.fit.mean err.abs.new.mean
## 787.00000 24.83665 25.27600 NA
## err.abs.trn.mean
## 24.82682## [1] "Features Importance for selected models:"## All.X..rcv.glmnet.imp Final..rcv.glmnet.imp
## Worry.About.Info.nonNA 100.00000 100.000000
## Privacy.Laws.Effective.nonNA 35.42580 54.819637
## Tried.Masking.Identity.nonNA 16.86637 14.337640
## Sex.fctrMale 13.17063 7.554599
## Anonymity.Possible.nonNA 10.98046 21.009376## [1] "glbObsNew prediction stats:"## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## label step_major step_minor label_minor bgn end
## 22 predict.data.new 10 0 0 102.835 115.757
## 23 display.session.info 11 0 0 115.757 NA
## elapsed
## 22 12.922
## 23 NANull Hypothesis (\(\sf{H_{0}}\)): mpg is not impacted by am_fctr.
The variance by am_fctr appears to be independent. #{r q1, cache=FALSE} # print(t.test(subset(cars_df, am_fctr == "automatic")$mpg, # subset(cars_df, am_fctr == "manual")$mpg, # var.equal=FALSE)$conf) # We reject the null hypothesis i.e. we have evidence to conclude that am_fctr impacts mpg (95% confidence). Manual transmission is better for miles per gallon versus automatic transmission.
## label step_major step_minor label_minor bgn
## 2 inspect.data 2 0 0 25.482
## 16 fit.models 8 0 0 60.262
## 22 predict.data.new 10 0 0 102.835
## 1 import.data 1 0 0 13.607
## 3 scrub.data 2 1 1 42.119
## 18 fit.models 8 2 2 81.083
## 17 fit.models 8 1 1 73.461
## 21 fit.data.training 9 1 1 96.877
## 12 manage.missing.data 4 0 0 53.071
## 20 fit.data.training 9 0 0 92.801
## 19 fit.models 8 3 3 88.956
## 15 select.features 7 0 0 58.413
## 11 extract.features.end 3 6 6 52.149
## 14 partition.data.training 6 0 0 58.081
## 10 extract.features.string 3 5 5 52.087
## 13 cluster.data 5 0 0 58.025
## 7 extract.features.image 3 2 2 51.946
## 9 extract.features.text 3 4 4 52.036
## 4 transform.data 2 2 2 51.825
## 6 extract.features.datetime 3 1 1 51.907
## 8 extract.features.price 3 3 3 52.000
## 5 extract.features 3 0 0 51.870
## end elapsed duration
## 2 42.118 16.636 16.636
## 16 73.461 13.199 13.199
## 22 115.757 12.922 12.922
## 1 25.481 11.874 11.874
## 3 51.824 9.705 9.705
## 18 88.956 7.873 7.873
## 17 81.082 7.621 7.621
## 21 102.834 5.958 5.957
## 12 58.025 4.954 4.954
## 20 96.876 4.076 4.075
## 19 92.800 3.844 3.844
## 15 60.261 1.848 1.848
## 11 53.070 0.922 0.921
## 14 58.412 0.331 0.331
## 10 52.148 0.061 0.061
## 13 58.080 0.056 0.055
## 7 51.999 0.053 0.053
## 9 52.086 0.050 0.050
## 4 51.869 0.045 0.044
## 6 51.946 0.039 0.039
## 8 52.036 0.036 0.036
## 5 51.906 0.036 0.036
## [1] "Total Elapsed Time: 115.757 secs"